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Hello colleagues
What are the differences and what is the indication of each audio encryption test such as SNR, Spectral Segment SNR, Peak Signal to Noise Ratio, Linear Predictive Code Distance, Log Spectral Distance Measure, Cepstral Distance, UACI and NSCR Analysis, Root Mean Square (RMS), Crest Factor, and mean square error?
Thanks
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Mohammed,
This is probably a question for signal processing specialists. My background is in audio engineering and subjective codec testing, not signal processing, but I have some knowledge of signal processing so I'll try to tell you what I can.
To start with, audio encryption is not a term I am familiar with. Encryption usually indicates some form of security encoding to restrict access to information. I have not seen this done with audio. Usually if someone wants to retrict access to an audio file, they put it through a ZIP-type program and add password access.
When digitised audio is processed to reduce the data volume, it is said to be encoded rather than encrypted. In everyday English, encoding and encryption have very similar meanings. In signal processing however, they have quite different meanings. I am guessing that your question is really about audio encoding rather than audio encryption. If not, please ignore the rest of my answer.
The reason for measuring signal properties prior to encoding is to optimise the encoding parameters to get the best possible perceptual quality at a given bit rate. There are several types of encoding systems in use. Some work in the time domain. Some work in the frequency domain. The audio parameters needed to optimise algorithms will depend on the algorithm in use. For a detailed answer to your question, you will need to consult a text on signal processing. Introduction to Digital Audio Coding and Standards by Marina Bosi, Richard E. Goldberg 2003 is a good place to start. It is now 20 years old, and I don't know if there are updated editions, but it covers several codecs that are still widely used. If you want information on specific codecs, try the IEEE Journal of Speech and Signal Processing or the Journal of the Audio Engineering Society.
I hope this is of use.
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Hello colleagues
What are the differences and what is the indication of each audio encryption test such as SNR, Spectral Segment SNR, Peak Signal to Noise Ratio, Linear Predictive Code Distance, Log Spectral Distance Measure, Cepstral Distance, UACI and NSCR Analysis, Root Mean Square (RMS), Crest Factor, and mean square error?
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Dear Mohammed Jabbar Mohammed Ameen,
You may want to review the data presented below:
An audio encryption scheme based on Fast Walsh Hadamard Transform and mixed chaotic keystreams
This paper introduces an audio encryption algorithm based on permutation of audio samples using discrete modified Henon map followed by substitution operation with keystream generated from the modified Lorenz Hyperchaotic system. In this work, the audio file is initially compressed by Fast Walsh Hadamard Transform (FWHT) for removing the residual intelligibility in the transform domain. The resulting file is then encrypted in two phases. In the first phase permutation operation is carried out using modified discrete Henon map to weaken the correlation between adjacent samples. In the second phase it utilizes modified-Lorenz hyperchaotic system for substitution operation to fill the silent periods within the speech conversation. Dynamic keystream generation mechanism is also introduced to enhance the correlation between plaintext and encrypted text. Various quality metrics analysis such as correlation, signal to noise ratio (SNR), differential attacks, spectral entropy, histogram analysis, keyspace and key sensitivity are carried out to evaluate the quality of the proposed algorithm. The simulation results and numerical analyses demonstrate that the proposed algorithm has excellent security performance and robust against various cryptographic attacks.
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Contents
  • 1 Definition1.1Decibels 1.2 Dynamic range 1.3 Difference from conventional power
  • 2 Alternative definition
  • 3 Modulation system measurements 3.1 Amplitude modulation 3.2Frequency modulation
  • 4.1 Definitionction
  • 5 Digital signals 5.1Fixed point 5.2Floating point
  • 6 Optical signals
  • 7 Types and abbreviations
  • 8 Other uses
  • 9 See also
  • 10 Notes
  • 11 References
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Speech signal processing common measurement method
tags: Voice measure Signal to Noise Ratio PESQ Log spectrum distance (Log Spectral Distance)
To-noise ratio (SNR)
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Neural networks recognise speech, that is, they turn speech audio signals into text. This is far from my expertise, I will assume though that the input is the amplitude of the signal over time. How is the input delivered in the system in the course of time? Is it in chucks, and if yes how long are they, or does it take one data point per time?
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Thank you for your response
Best
Chris
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I'm working on a project on speech recognition of ALS patients.
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I'am working on ALS detection problem. You can use our voice database:
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I am a beginner in this field. I want to learn basic audio deep learning for classifying audio. If you have articles or tutorial videos, please send me the link. Thank you very much.
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Hi Tim Albiges
Wow. This is very helpful. Thank you so much.
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I will be interviewing clergy members.
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I have had good results producing transcripts using the online program, otter.ai. Of course, no software program will be perfect, so I recommend that you carefully read and edit each transcript as a first step in your analysis process.
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I would like to get them to make a vocal sound related to a texture. This would have them use their voice to answer the question and I would collect the audio recording to use as research in my paper.
Thank you,
Colm
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Has anyone used Phonic? https://www.phonic.ai/product/surveys . I would like to use it for speech research, but I don't know anyone who has yet.
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Why not ? We suspect the answer might be useful for calculating reverberation time in addition to sound intensity field in audio rooms of different geometries.
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Hi Ismail,
Sabine's formula is still relevant in room acoustics, and the formulas that came after, such as Eyring's, Kuttruff's or Fitzroy's, are heavily inspired by Sabine's work. Admittedly, it it not perfect and the requirements of diffuse sound field and uniformly distributed absorption make it inaccurate in the majority of rooms.
However, many researchers and acoustic consultants still use it in their work and related software, such as Odeon or Catt-acoustics, usually include the option of calculating reverberation time of a modeled room with Sabine's formula. On top of that, the equation is also used in e.g., sound absorption coefficient estimation. For all of those reasons I personally think that it is still worth it to study Sabine's formula and try to improve its accuracy.
Please see the paper that I and my co-authors published just recently in the Journal of the Acoustical Society of America, Calibrating the Sabine and Eyring formulas : https://doi.org/10.1121/10.0013575 It discusses the applicability of Sabine's and Eyring's formulas and offers a method to improve their accuracy.
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1.I am researching on producing Spatial Audio on Loud Speaker.Hence i am trying to learn how it is relating to Filters.
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Dear, I suggest that you read this research papers, which were about IIR filter.
Regards
1. Convergence Rate For Low-Pass Infinite Impulse Response Digital Filter
2. Digital Filter Performance Based on Squared Error
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In ERP P300 recording ( Audio stimulus), the P3 wave shows up as double hump, where to pick Peak for Amplitude and latency?
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Good Evening Blaine. I do have a incertitude about splitting the P300 wave into P3a and P3b component for such waves. Do you have any input on this. Please share. Thankyou – 📷Ranjit Savita Gaur Kumar
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Hello everyone,
Could you recommend courses, papers, books or websites about wav audio preprocessing?
Thank you for your attention and valuable support.
Regards,
Cecilia-Irene Loeza-Mejía
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librosa is a python package for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems.
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Does anyone know any real free online tool, software, or application for the purpose of transcribing recorded conversation and audio files?
I'm conducting conversational analysis research based on a corpus collected from EFL learners. I'm beginning to wonder if there is any free online tool for the transcription of audio files?
I'll be grateful if anybody can help
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Try google cloud speech-to-text - speech recognition (when you register in google cloud, they give you some free minutes to transcribe).
Try also https://trint.com/ , https://www.happyscribe.co/ and https://sonix.ai/ . In all of them the duration of free transcribed files is limited but you can register several times with different e-mails...
Best of luck!
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I am doing linguistic research into the songs of a minority language and would appreciate any suggestions of books/papers covering methodologies for conducting such research. I have audio recordings of songs with transcriptions and translations and would like to start detailed analysis. Thank you.
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You are welcome, Jonathan; I was happy to help. Feel free to contact me at any time.
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Dear colleagues and experts
When I encrypt audio using one of the chaotic map for example logistic map and then DNA cryptography rules, how can I estimate the keyspace of all system
Thanks
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We've been all round the houses with this one, and apologies if there is already a thread around this, but I can't seem to find a decent answer.
We've tried using Audacity to record interviews in this way, but it only records the interviewee, and not my questions to them. I need something that will record everything that I hear in my headphones while I'm conducting the interview - which of course is my own audio, and audio from the interviewee.
This is probably a newbie question - but any ideas?
Many thanks for any helpl/advice
Mark LIddle
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Thanks Christian. I've just had a quick look at the OBS information, and that does look useful. I'll have a play with their software and see if we can do what we want using that - many thanks for that reference, as we've not used OBS before. (On the face of it, what we want to do seems like such a simple thing technologically - very odd that everyone has to find these workarounds.) Cheers for that - I will let everyone know how we get on.
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Given a music audio file, I want to find out it to which genre it belongs to. To do this i want to know what the most important features to be extracted from the audio file to feed to a classification algorithm.
If anyone has worked on this type of work, please share your experiences.
Regards,
Sudheer
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Here is our work: a deep neural network architecture designed for audio classification. Code provided. Do not forget to cite our work
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Hello everyone,
I am looking for links of audio datasets that can be used in classification tasks in machine learning. Preferably the datasets have been exposed in scientific journals.
Thank you for your attention and valuable support.
Regards,
Cecilia-Irene Loeza-Mejía
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In our work we used UrbanSound8K .
Here is our work with code provided. Do not forget to cite our work
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I use some applications, but I seek more useful options. I wonder you're the applications that you are used to and your experiences with them. Thanks for your replies in advance.
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Good day
Best regards
Ph.D. Ingrid del Valle García Carreno
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I was trying to build the model but while transforming the wav files to spectrographic images it was taking quite a long time. In 7 hours I was able to convert only 1500 wav files out of 27000. And then my system crashed. If I were to do it once more it's going to take around 4 days and I don't think that's feasible. Can someone help.
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Yes , It is possible
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I’m looking for a camera trap that can be set out in the field to function as a camera trap but can simultaneously be programmed to take a video with audio for a set time each day. Does anyone know of a camera trap that has this capability?
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It might be easier to just have one camera set for timelapse video and another camera for motion detection.
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Hi everyone! I’m looking for an open software toom to convert .mp3 audio files to text. We are analizing oral narratives from children and our raw data are audio recordings. while this seems pretty straightfprward, we haven’t been able to find a free software that solves this properly.
thank yo so much for you help!
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Thank you so much for your answers! We do need that the software is open source (or at least, free) and that it its able to work with audio files directly. I'll try the alternatives that meet these requirements. Do you know if there are any language limitations? We are interested on transcribing audio files in spanish.
Best regards.
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I am looking for the best online teaching platform (due to Covid-19).
The features I need would be to
1: share audio, video lecture utilizing less data (due to problems of data costs for the students in developing countries)
2. bear weaker/interrupted internet signals during live lectures.
3. Offcourse free of cost.
Need comments,
Best wishes,
Zeshan.
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Zoom, Microsoft teams, and blackboard
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Can we use the NPCR and UACI to test the robustness of audio encryption against differential attacks? What is the range of these two for a good encryption system?
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I am working upon audio-video data to detect anomalies. I want to know that between audio and video, if audio detect anomaly and video does not then what is probability that audio detect correctly or video detect correctly?
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Hi Taric,
I have used high speed cameras to analize acoustical behavior of rotating or moving parts. It is also usefull to receive answers of damping.
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porter stemmer algorithm is used by python for English and other languages
but i want to create stemmer algorithm for local language hoe i can
i want full resource like book, video,audios etc.
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i hope it will be usefully for you https://snowballstem.org/algorithms/
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I am using a speech in noise task called the BKB sentence test (Australian version). At this stage, Audacity had enabled me to slice a large audio file into very precise trials (.wav files) so that I can include them in a PsychoPy experiment and align them with an eye tracker to get pupillometric recordings.
I now need to adjust the two channels of each trial to precise signal to noise ratios (SNRs) and combine the 2 channels into 1 channel .wav files before inclusion in my experiment. I want to have SNRs at 4 levels.
I can't seem to find a way to do this in Audacity. But it seems like something that should be relatively easy, if I knew the right place to look...
Thanks in advance for any advice
Jennifer
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It's been a while since this question was asked, but there are a few available online sources to tackle the SNR mixing process automatically if you are using multiple files (as it probably will happen if you are using word/sentence recognition tasks). I am putting them here for future reference.
The second one is for to use with Praat:
The last one is for to use with Python:
Hope that would be helpful
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I have an audio file running in the format of mm:ss, but i want an audio file to run in this format mm:ss:msms(milisecond). I have used this format of converting by VLC media, but it is not running. LINK: https://www.youtube.com/watch?app=desktop&v=A9yq8qT0hqY
So is there any way I can get audio file in miliseconds also?
It will be a great favor
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You should be able to view the timeline of your audio files in milliseconds if you wish, but VLC is probably not the tool to use for this.
I'd suggest using an audio editing tool like Audacity (https://www.audacityteam.org/) to open your audio fles. It is free.
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Hi, I am currently working on a project which requires me to implement the ICA algorithm in real time, to be specific, I am trying to separate the noise from the audio. When I perform the algorithm in offline, it works fine despite the amplitude of the separated audio is a bit soft. However, when I implement it in real-time, the separated audio becomes very soft. Any source code that I could refer to solve this kind of problem. Thanks.
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What you are describing appear to be two different problems, one compounding the other
For the first problem
1) Which ICA implementation are you using?
For the second problem
1) For the real time capture what sampling rate are you doing the capture?
2) what are you using to capture the signal in real time(e.g. are you using ALSA on Linux or are you using Windows)?
Note that the RM Cortex-M7 of the Teensy 4.0 is a slow processor compared to an Intel or AMD
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Is it possible for an audio tone to be shifted by some factor due to the change in temperature in between the transmitter and a receiving microphone?
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I agree with Tim Ziemer answer.
Is it possible to validate this through some simulation somehow? I used I-SIMPA to model a cubic room in which a source is generating a tone (say at 200Hz). Now, I did 2 simulations, one with 4°C and the other with 100°C. I want to see some shift in the received frequency. However, I am unable to find such a shift.
Is this the limitation of I-SIMPA or I am doing it wrong?
Moreover, is it possible to validate this by some basic equations (e,g. in MATLAB)?
Thanks
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Hi
I’m looking for a solution for 2 way audio connectivity between a remote site with wireless headphones and a PC. In the remote site there is only possible to get power from a POE (Power Over Ethernet). The PC contains our own backend software and application.
Is there a solution based on Wi-Fi headphones?
What suggested communication software to use (WebRTC?)
Any other ideas?
Thanks
Doron
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Intercommunication-Microphone/dp/B086C1YGBS
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Hello,
I have been working on a project where I have to mix multiple audio signals of the same source coming from different slave smartphones on one master smartphone in a distributed way. Now I have aligned multiple audio packets (Packet level synchronization) in real-time (still unable to do sample-level synchronization) but when I mix them I get a comb filter effect.
My packet contains 40ms of data at a sampling rate of 48KHz. How can I eliminate this effect?
I am more into ways of making it smooth rather than subtracting delayed signals? Is there any kind of filter somewhat "AntiComb Filter" to make this happen?
Regards,
Khubaib Ahmad
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George Slade Thank you for your help. We figured out that adding magnitudes of both signals while using a phase of only one signal not only enhances our voice but also diminshes the spatial texture of voice.
Thank you for the help.
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Dear all,
Please find the call for a special issue on " Machine Learning Applied to Music/Audio Signal Processing" in MDPI Electronics at
Thanks and we are looking forward to your contributions!
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Dear Colleagues,
The applications of audio and music processing range from music discovery and recommendation systems over speech enhancement, audio event detection, and music transcription, to creative applications such as sound synthesis and morphing.
The last decade has seen a paradigm shift from expert-designed algorithms to data-driven approaches. Machine learning approaches, and Deep Neural Networks specifically, have been shown to outperform traditional approaches on a large variety of tasks including audio classification, source separation, enhancement, and content analysis. With data-driven approaches, however, came a set of new challenges. Two of these challenges are training data and interpretability. As supervised machine learning approaches increase in complexity, the increasing need for more annotated training data can often not be matched with available data. The lack of understanding of how data are modeled by neural networks can lead to unexpected results and open vulnerabilities for adversarial attacks.
The main aim of this Special Issue is to seek high-quality submissions that present novel data-driven methods for audio/music signal processing and analysis and address main challenges of applying machine learning to audio signals. Within the general area of audio and music information retrieval as well as audio and music processing, the topics of interest include, but are not limited to, the following:
- unsupervised and semi-supervised systems for audio/music processing and analysis
- machine learning methods for raw audio signal analysis and transformation
- approaches to understanding and controlling the behavior of audio processing systems such as visualization, auralization, or regularization methods
- generative systems for sound synthesis and transformation
- adversarial attacks and the identification of 'deepfakes' in audio and music
- audio and music style transfer methods
- audio recording and music production parameter estimation
- data collection methods, active learning, and interactive machine learning for data-driven approaches
Dr. Peter Knees
Dr. Alexander Lerch
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Another opportunity related Machiine learning signal processing Music and Audio and Interfacing concepts with Computer Science and Inter Disciplinary subject call far paper for 13th International Conference on Human Computer Interface also an opportunity for submit research articles from UG and PG and also research student and acadamic professional and Industrial Expert.
Kindly see the following call for paper
at Kent University United States.
  • @Prof Kim andJ Prof Javed Khan (Kent State University, USA)
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Given a huge dataset of audio signals say of 10s or 60s ( which contains songs, recitation, political speeches, etc. ), are there traditional ML or deep learning techniques to cluster those audio files automatically ?
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If so, what platforms have you had success with?
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Hi Sarah Wayland , so far only by data for the survey.
Perhaps u could provide a link for the audio as an option.
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I need suggestions for research topics that involve both speech and image, that use/need expertise in both speech/audio and image.
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Dear Rizwan,
An interesting topic is detection of truth or falsehood in face-to-face interviews.
BTW, we published two papers about detection of truth or falsehood in textual stories:
(1) HaCohen-Kerner, Y., Dilmon, R., Friedlich, S., & Cohen, D. N. (2016). Classifying true and false Hebrew stories using word N-Grams. Cybernetics and Systems, 47(8), 629-649.
(2) HaCohen-Kerner, Y., Dilmon, R., Friedlich, S., & Cohen, D. N. (2015, October). Distinguishing between True and False Stories using various Linguistic Features. In Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters (pp. 176-186).‏‏
Best regards,
Yaakov
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My background is in Speech/Audio enhancement and separation. I want some suggestions for using my background in Natural Language Processing topics.
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Spontaneously, I have not a concrete suggestion, but I am curious where you are taking the Náhuatl-recordings from? I am now working with Sabina de la Cruz, an L1-Nahuatl speaker, who has a 10-month-old daughter. She is currently trying to establish Nahuatl as the language of use in her family so that her little daughter also acquires Nahuatl as her L1.
She has been recording the babble production of her child and I am in charge of analyzing the data.
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Hello. I am conducting a psychological research study with participants in the UK, and I want to know what privacy and data protection rules there are about recording participants' microphone audio and camera video for research purposes. We have a video-conferencing simulation with a virtual human (recorded actor) and would like to record short clips of participants' audio and video during the video interaction. Is this possible, and, if so, what types of informed consent and data protection need to be provided?
Thank you so much.
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Your question is very interesting, thank you for taking me into account
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We know that the pre-processing of speech recognition includes echo cancellation, de-reverberation, audio enhancement, noise suppression, and so on. Is there a comprehensive toolbox/code for that purpose that can be a start point for a beginner?
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Firstly you should have some fundamentals of speech processing (speech signal basics and features, DSP procedures like FFT, STFT, Convolution, …)
In addition to important signal processing techniques like finding energy and the power spectrum of the speech signal, autocorrelation, silence speech removal methods, etc.
I suggest this good book as a start:
"Fundamentals of speech recognition" by Lawrence Rabiner and Biing-Hwang Juang.
Regarding your question about a toolbox, Matlab has Deep Learning Toolbox to design, train, and analyze deep learning networks.
You can preprocess audio input with operations such as echo cancellation, audio enhancement, noise suppression by using datastores and functions available in Deep Learning Toolbox and other MATLAB toolboxes like Signal Processing Toolbox which offer functions, datastores, and apps for processing, and augmenting deep learning data.
For this see:
Which gives an example that shows how to train a deep learning model that detects the presence of speech commands in audio.
Best regards
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I have an audio set, and I have to determine the normal and abnormal sound. I have preprocess the audio data using MFCC. I got output as (audio Files, MFCC coefficients, MFCC vector, 1). When I passed this input to convLSTM, it shows an error that it requires five dimension input. But I am passing 4 dimension input. So how can I increase the dimension or is there any way to pass 4 dimension input to convLSTM.
Kindly guide me.
Regards
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I am working on audio dataset. The dataset contains train and test audio files. The train audio file contains crowd environment where only human are murmmering and chattering. The test data contains these chattering voices of human alongwith gunshot, car passing, and accidents.
My question is which features are good for extracting information from this kind of training data who has no specific sound, just the chattering voices?
Regards
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Thanks for the spectrogram, Tariq. It looks quite uniformly random, so I can't see any obvious features from it. There is a pale stripe across the bottom below the 64 Hz band which looks more constant than everything else - is there some low frequency hum in your sample?
And can you explain a bit more what you mean by "useful information"? What is the aim of your study?
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I have an audio dataset. I have process it and built an array as:
(audio Files, number of features, feature vector, 1)=(18, 64, 1688, 1)
But when I pass this to my model (file attach below), It shows me following error:
ValueError: Input 0 of layer sequential_54 is incompatible with the layer: expected ndim=5, found ndim=4. Full shape received: (None, 64, 1688, 1)
And when I again reshape it to get ndim=5 as:
(audio Files, number of features, feature vector, 1, 1)=(18, 64, 1688, 1, 1)
It again gives me an error says that:
Input 0 of layer conv_lst_m2d_83 is incompatible with the layer: expected ndim=5, found ndim=6. Full shape received: (None, None, 64, 211, 1, 128)
I do not know how to resolve this issue, and I have follow this link that https://stackoverflow.com/a/47665700/12559855 but my issue is not resolved.
Kindly guide me in this regard. It will be a great favor.
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It's a basic error if the model gets the wrong dimension of inputs. Keep in mind that the input should have a format like (N, d1, d2, ...dn), where N is the batch size of your input.
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We have several video and audio files that we would like to annotate to create a scoring algorithm. We would like to know what applications (mobile or web) or annotation tools researchers are using in order to make an informed choice.
  • What are the benefits?
  • What are the disadvantages?
  • Your experiences with the tool
Thank you very much for all your advice,
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personally, i would say using it gave me a wider range of python libraries. this is the only annotation tool i have used though so i cant really make a comparison chart with the other annotation tools available.
read more on it https://prodi.gy/docs
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We need your help to evaluate a deep learning model for audio restoration. Please fill in this survey (it only takes 20 minutes) or share it with your network. https://lnkd.in/gYY-32P Thanks a lot.
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Greetings! Sounds high and low or perceived less faithfully compared to the original. Medium sounds and the combination of medium sounds with echo sounds were best perceived compared to the original. Good luck!
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Hello! I am hoping to find a speech recognition tool that can automatically determine the length of each speaker's speaking turn in seconds, based on an audio or video recording of a conversation. I'd like to know the average length of each person's speaking turn in a given recorded conversation. Also, I'm looking for an accurate measure of how much overlap there was between different speakers. Ideally, the tool would automatically detect that multiple speakers were talking at the same time, and give me either a percentage or number of seconds in the conversation sample that more than one person was talking.
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Yaakov J Stein That is very helpful, thank you very much!
Heidi
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I am trying to build a voice cloning model. Is there some scripted text I should use for the purpose or speak anything randomly?
What should be the length of the audio and any model suggestions that are fast or accurate?
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Text to Speech Synthesis is a problem that has applications in a wide range of scenarios. They can be used to read out pdfs loud, help the visually impaired to interact with text, make chatbots more interactive etc. Historically, many systems were built to tackle this task using signal processing and deep learning approaches.In this article, let’s explore a novel approach to synthesize speech from the text presented by Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno and  Yonghui Wu, researchers at google in a paper published on 2nd January 2019.
Regards,
Shafagat
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Hello, I have 26 hours of audio data. This is huge data to manually label every 2s data frame as a scream or not. Is there any idea to do at least a portion of it automatically to save time?
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Thank you everyone. That helps a lot.
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Hello,
I would like to apply an self-attention mechanism on a multichannel audio spectrogram, so a 3D tensor. In the original Transformer paper, self-attention is applied on vectors (embedded words) within a kind of temporal sequence. On my multichannel spectrogram, I would like to apply self-attention both on the temporal and frequency axes, so that the analyzed vectors are "through" the channel axes.
On tensorflow.keras MultiHeadAttention layer, there is a attention_axes parameter which seems to be interested for my problem, because I could set it up to something like (2,3) for a input feature of shape (batch_size, nFrames, nFreqBins, nDim) and hope attention will be applied on the wanted dimensions. However I don't understand how it works since it's different from the original Transformer paper, and I don't find any relevant paper addressing self-attention in several dimensions in the same manner.
Also the source code doesn't help, the algorithm is split into several sub-modules which are not self-explanatory to me.
Any insights would be precious!
Thanks a lot
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The idea of Transformer is obtaining the global reletionships beetween embeddings (feature vectors, pattern units) in the input set. For audio data the local frequancy vector characterizes the time window. So you can use frequancy axis of your 3D tensor at each time point as embedding.
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i.e., I need to know, the best audio compression algorthims.
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As told before, it complety depends on what do you mean for "best", Personally, I would say that the best has been the mp3 format, given the great impact it has had on society and its good efficiency considering the limitations of human hearing, but for a test laboratory that would perhaps be one of the worst
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I am conducting research on automated audio editing, by artificial intelligence, to remove oratory addictions, such as stuttering, repetition of sounds like "ahaa", "hummm", and other recording noises that affect the sound quality of the audio.
I ask you to indicate, please, articles and other bibliographic materials on the topic, in addition to any other suggestions that you have.
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Here is a related project:
For C#, there is library NAudio by Marc Heath that comes with sample projects.
Regards,
Joachim
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Does anyone have suggestions on software that has been particularly helpful in transcribing and analyzing data recorded during focus groups? We have some money from a grant that can be used to purchase licenses. Any insight would be helpful. Thank you.
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Does anybody have experience using Raven Pro for the analysis of recorded literature?
Thank you,
Víctor
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Hello,
I have been working on acoustic echo cancellation while following the research paper :
Since I am working on real-time audio data so my problem is as follows:
  • I have a buffer that stores Far-end (packets being played by phone) data in terms of 21.33ms chunk equivalent to 1024 shorts.( Sampling rate 48000 Hz)
  • A near-end packet is recorded i.e 21.33ms of data with the same format as mentioned above.
Problem Statement:
  • Let's suppose we have a Far-end packet containing the word "hello"
------------------------------
| |
| H E L L O |
------------------------------
is being played by phone and now its echo is being recorded in a Near-End packet. Now the cases arise here are that this could be recorded as:1
------------------------------
  1. | |
| H E L L O |
------------------------------
Hello completely recorded in one packet.
2. ------------------------------ ------------------------------
| | | |
| H E | | L L O |
------------------------------ ------------------------------
Distributed in two Near-end packets
3. ------------------------------ ------------------------------
| | | |
| H | | E L L O |
------------------------------ ------------------------------
and so on random distribution of far-end audio between multiple chunks of near-end audio.
Now, I want to detect echo and perform cancellation. That can be done if I get an accurate chunk of far-end whose echo is in the near end chunk. Right now I am making overlapped chunks with the shift of 1 sample equivalent to 0.020833 ms of data. But this is quite computationally over expensive.
So 2 questions arise here:
  1. What should be the optimal length of overlapping?
  2. What could be the minimum resolution of acoustic echo in terms of time in ms? i.e minimum echo delay change? Can it be like 0.1ms or 0.02 ms ?
I hope my question will be cleared. I tried to explain it yet keep it concise.
Any help will be appreciated.
Regards,
Khubaib Ahmad
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For the part relating to the listeners, yes. For the recognition and measurement an earlier echo detection may be useful for potential risky ones. I have never processed this myself, but have a lot of room acoustics measurement and prediction experience. To figure out if there are echoes in rooms or measured impulse responses listening to the impulse responses is the best method in my experience. You need to take that further. Althoiugh video conference systems and possibly mobile phones have such algoritmes already. Theree should be vast amounts of literature on the phenomenon.
I also have a conference article on the acoustics of coupled rooms. The environments needs to be drier than usual to work well. It is impossible to get rid of reverb that are already on the signal..
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Hello,
I have been working on the decorrelation of audio signals while following this paper :
So far I have generated impulse responses in such a way that magnitude is 1 in all frequency spectrum, while random phase generation between -π to π. I correlated these signals and I am getting the required correlation.
Now moving on, when I convolve this impulse response with my audio signal, results in the case of decorrelation are good but the audio becomes noisy and distorted. I am attaching the reference wav file and and decorrelated wav file.
Some points regarding computation:
  • All audio data computations are in float range [-1.0:1.0].
  • I am using Python to perform all the calculations
  • The input reference file is in Short (PCM 16 bit) format which is converted to float range [-1.0:1.0].
  • I have saved convolved_decorrelated_output.wav in both formats PCM 16 bit short and Float as mentioned above. Still getting the same results. So there are no issues regarding data type conversion as per my knowledge.
Any help will be appreciated.
Regards,
Khubaib Ahmad
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great !
It is time to mark my answer as best answer!
Thank you!
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Hi!
I'm looking for an online platform/solution that allows me to create a form where the respondents can listen to audio files and then type what they have heard right below the audio file. Preferably, I'd be able to export all the answers to an Excel file.
I tried JotForms, but the only audio files I could insert were SoundCloud links, which is not good at all as they have to be public to work.
This is for my thesis, which investigates the effects of an ASR-based application on the intelligibility of Brazilian speakers of English.
Thank you! I hope I didn't miss an easy/obvious solution for my problem.
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You can use an online form/survey tool (e.g., Google Forms) with embedded audio functionality as pointed out by McGarrick (2021), or you can use Listen N Write as illustrated by Ashwin (2019).
Ashwin. (2019, November 7). Transcribe text from audio recordings with listen N write. gHacks Technology News. https://www.ghacks.net/2019/11/07/transcribe-text-from-audio-recordings-with-listen-n-write/
McGarrick, B. (2021, February 28). How to add audio clips to your survey. Attest Intercom Help. https://intercom.help/attest/en/articles/1631376-how-to-add-audio-clips-to-your-survey
Tutorial for adding audio to Google Forms is on the link below.
Good luck with your thesis,
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Hi,
I am transcribing a lot of audio interviews. Currently, I am using Windows Media Player and if I have it hit pause and go back to clarify what was said I have to use the slider down the bottom which often takes me too far. Just wondering what software others are using which has a back key option that will allow you to go back say 10 or 15 seconds per hit. This will save me a lot of time.
Thanks in advance
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Perhaps an audio editor or a DAW might be of help. The allow you to put markers and see your audio through waveform representation. Something like Wavosaur or Audacity, which are both free or, more advanced, Reaper which has unlimited trial (so is virtually free, but also very affordable for non-commercial use).
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Hi,
I have been working on multi-loudspeaker and single mic acoustic echo canceller ( more than a typical stereophonic echo canceller). I have researched and came to know that we have to decorrelate the signals in order to correctly identify the estimated echo signal (i.e replicate impulse response). So, I want to know if there are any decorrelation techniques for let's say N number of loudspeakers?
Please share the link if possible.
Regards,
Khubaib Ahmad
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Hello,
Here are 2 papers that are related to your problem. They can be uploaded following this link : http://www.buchner-net.com/mcaec.html
  • H. Buchner and S. Spors, "A general derivation of wave-domain adaptive filtering and application to acoustic echo cancellation," Proc. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, Oct. 2008.
  • H. Buchner, "Acoustic echo cancellation for multiple reproduction channels: From first principles to real-time solutions," Proc. ITG Conf. on Speech Communication, Aachen, Germany, Oct. 2008. Best Paper Award.
I hope this will help,
Pascal
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I want to convert audio signal(live recorded using microphone) to digital which I can transmit that digital signal over Bluetooth. I have designed a signal conditioning circuit which has a series of pre-amplifier, filter and DC level shifter, output of signal conditioning circuit is to convert in to digital form. For this conversion I want to use microcontroller. And also suggest Bluetooth module for same.
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welcome!
Adding to prof. Jeorg one other important petameter is sampling rate which depends on the type of audio signal. If it is speech then the sampling rate is standardized at 8 ksamples/s.
As for the audio signal in the frequency range from 20Hz to 20kHz.
The standard sampling frequency is 44.1 kHz.
For any audio signal with a limited bandwidth BW, we can apply Nyquest sampling theorem.
fs>= 2 fs,
Normally fs is taken greater than 2 fax with fmax the maximum frequency in the audio signal. This is for easier interpolation by a law pass filter.
The other point is that in order to save storage capacity and transmission rate,
one may compress the signal with an MPEG standard such that MP3.
All this depends on the application.
Best wishes
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I aim to take a raw audio file as input. And my final objective is to convert that audio into an, unique for each audio, ID. Is there any python library to do it? One which takes audio as an input.
Or One which can at least convert it to something from where I can further convert it to unique IDs?
Thanks in advance.
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Is there a need for a semantic link between element IDs and the contained audio signals? If yes, the previous answers might guide you to your success, if that link is not strictly needed however, I would suggest to just assign an arbitrary ID to the file. There are manifold ID generators for any programming language, but even a counting ID might suffice as long as you do not wish to interprete the ID itself. If the audio signal does not need to be encoded, but some linkage to the file content is needed because of different files with the same content, meta data indexing might also help.
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Hi all,
I'm currently undertaking a research project for my dissertation and will be using Audio Moth to recording vocalizations from Bleeding Heart Doves. Their calls are relatively low at 500 htz. I'm looking for any software suggestions that might be useful in analyzing these recordings as well as isolating potential vocalizations from these birds.
Any suggestion is a great help as I don't know where to start.
Thanks!
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Hello,
I wanted to know that related to an audio signal which has Amplitude, Frequency, and Phase as key characteristics. Do MFCC coefficients depend on them? Are there any other factors on which MFCC coefficients depend?
Regards,
Khubaib
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Tim Ziemer Thank you so much. This is what I wanted to know. Thank you for the clarification.
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Hi everyone,
I need to plan an online experiment in which each participant should watch a series of screens each containing an image (the screens/images must appear in a randomized order). During the whole series of screens, an audio file should be played, it should begin at the first image and it should end at the end of the session.
I have a Qualtrics account, but I'm not able to implement this kind of procedure. In general, as I build a page with the audio player, the audio won't be playing anymore as soon as the next screen is on. On the contrary, I need the audio to be playing in the background during the whole presentation.
Could I achieve my aim by programming something in Python / Java / Node JS / HTML? Or should I change software? Any suggestions?
thanks in advance for any help
all the best,
Alessandro
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Our lab at the University of Birmingham have just started using Gorilla.sc to build online experiments. So far, I have found Gorilla.sc really intuitive and easy to use and so I would definitely recommend it to other researchers. I'm not 100% sure whether you will be able to have the file playing in the background for the whole experiment, however, there is an option to have audio files playing in on each trial on Gorilla.sc so you might be able to?
If you do choose to use Gorilla.sc, some of the following webpages could be helpful to you! I referred to some of the following webpages to get an idea of how to build a task successfully:
Video walkthroughs that give a speedy introduction to using Gorilla.sc, some of which I found quite useful: https://gorilla.sc/support
Sample Code: https://gorilla.sc/support/script-samples#altercontent. I found the sample code really useful as there were examples for altering or adding content to the task via script (i.e. forcing participants to be in fullscreen when taking part, changing background text and colour, implementing live scoring etc.).
Hope this helps! Wishing you all the best with your research!
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Dear Colleagues, please suggest which is the best and user friendly open source software for audio signal analysis with most of the Scientific tools to audio signal analysis.
Thanks and Regards
N Das
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Thanks dear @Shahin Mardani, @Mila Ilieva, @Mani Entezami for your response to my discussion.
Dear @Mani, I will install the sonicvisualiser . Thanks for your support.
Best Wishes
Regards
N Das
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Hello everyone!
I am working with a comparatively small audio dataset for classification task. I was searching for a means to ensure robust performance and thus, came across the following paper [1] that performed test-time augmentation on the test set. The test-time augmentation includes four procedures: augmentation, prediction, dis-augmentation and merging as follows:
First, the test images are augmented (rotation, flip) as done on the training set. Prediction is performed on both the original and the augmented images, then they revert the transformation on the obtained predictions (dis-augmentation). Merging can be done by employing the majority voting criterion with several additional steps.
I am confused regarding the dis-augmentation and the merging steps in case of time-frequency representation of audio signals (spectrograms, scalograms). Is this method applicable for time-frequency representation of audio signals? If any of you can enlighten me in this regard, that will be really helpful. Thanks in advance!
[1] Moshkov, N., Mathe, B., Kertesz-Farkas, A. et al. Test-time augmentation for deep learning-based cell segmentation on microscopy images. Sci Rep 10, 5068 (2020).
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audio signals are presented as a change in the amplitude in the time domain in your reference paper. For time frequency representation, the signals are presented as a frequency change in the time domain, not amplitude. You can process your augmentation, by the same manner. Just replace the amplitude values in your signal vector by the frequency values. Try it.
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Hi all,
This is my first time posting to Research Gate, I often look through posts for similar research questions I have of my own. I am in a Research Methods grad course and I am suppose to create a statistical hypothesis and choose a testing method for my study. I have developed this problem statement and research questions:
Problem Statement
The problem to be addressed is, how to effectively deliver take-over-requests (TOR) to get the quickest response time from a driver.
Research Questions
Overarching research questions to this problem statement is:
1) What type of take over request (TOR) is warrants the most efficient response time by driver?
2) How do response times between elderly drivers and young drivers differ?
3) Do elderly drivers respond better to certain stimuluses?
I want to test multiple TOR methods such as: Visual/audio, audio/haptic, audio, haptic and visual against two categorical age groups (young and old drivers), with my dependent variable being time.
Does a Two-way ANOVA sound appropriate for this? I've asked two different professors, one gave me a cryptic, you can't do that, type answer...and another one gave me an answer that i'm still uncertain of. Please let me know if i'm headed in the correct direction with this of if there is a better way.
Thank you.
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I'm not sure why the first professor stated that a two-way ANOVA won't work. Based on your description, it seems you have two categorical independent variables (i.e. factors) and a continuous dependent variable. A two-way ANOVA is appropriate with this design.
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Al-Hussein bin Talal University- Ma’an-Jordan, decided to adopt a system that makes providing a complete audio and video recording of the conduct of discussions of all undergraduate and doctoral dissertations in the university compulsory as a positive measure that enhances quality, transparency and confidence and reflects a clear picture of the quality and sometimes seriousness of the research work and student capabilities and benefits the rest of the students in the process of preparing for their thesis discussions and increases the knowledge content of university libraries. Al-Hussein bin Talal University invites world universities and the scientific community to express an opinion on the pros or cons of this procedure and accreditation when convinced of its usefulness. Najib Abou Karaki / President of Al-Hussein Bin Talal University Juin 2020.
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Adopting this idea will increase the quality of the research, especially the supervisor will take it seriously to defend his /her reputation.
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It is a well known fact that in audio reconstruction magnitude and phase both information is required for a high quality synthesis. Many reasearch are focused on converting audio into spectrograms and then use only magnitude (2D image) as an input to GAN network, which generates a similar spectrogram (only magnitude) and the phase is approximated at the end using differnt techniques such as Griffin Lim.
I have been searching for the resarch who has used both magnitude and phase image in GAN network. But to my surprise, I don't find any work that use both Magnitude and phase as an input to GAN. How come it is not been done or I am not able to search?
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There is one research that uses both magnitude and phase information, here is the link:
This research has focused in Music domain using data NSynth ( https://magenta.tensorflow.org/datasets/nsynth ) specifically used for Music with labelled notes and pitch.
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The audio recognition is one of the deep learning application
For fast training , the audio signal is divided into set of rows before the feature extraction and running the deep learning model are done
How we can do this division in a python code ??
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I suggest to focus your work or your research for deep learning by using RNN architecture, for not spending your time. RNN use LSTM layers, it’s employed for signal processing. You can find the documentation in website TensorFlow.
I hope that be Claire for you and helpful.@ Reema Ahmad
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I am trying to build a joint classifier for Bimodal Sentiment Analysis which takes two modalities(audio and video files) as inputs. Any suggestions, how can I concatenate the below audio and video features to train a CNN based deep learning models?
Audio features:
X_aud = np.asarray(aud_data)
y_aud = np.asarray(aud_labels)
X_aud.shape, y_aud.shape
((1440, 40), (1440,))
Video features:
X_img = np.asarray(image_data)
y_img = np.asarray(img_labels)
X_img.shape, y_img.shape
((11275, 256, 512, 3), (11275,))
Any help would be highly appreciated. Thanks In Advance!
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I would suggest starting with LSTM encoder for the temporal dimension of both inputs. For the fusion within a convolutional neural network I recommend a similar architecture used in this paper:
This architecture considers multiple fusion levels for inputs that are different in their level of abstraction and information.
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Hi All,
I have an audio database consisting of various types of signals and I'm planning to extract features from the audio signal. So I would like to know whether it's a good idea to extract basic audio features (eg MFCC, Energy ) from the audio signal with a large window (Let's say 5s width 1s overlap) rather than using conventional small frame size (in ms). I know that the audio signal exhibits homogeneous behavior in a 5s duration.
Thanks in advance
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Due to the dynamic nature of audio signals, features calculated from large window sizes becomes an average value over the window rather than instantaneous values. On the other extreme, for window sizes less than 5 ms there might too few samples to give a reliable evaluation.
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Hi,
We at Tata Medical Center, Radiation Oncology Department have been developing an online software that allows us to collect patient-reported outcomes. The software allows users to set up and deliver multiple forms and questionnaires and also is easily amenable to be translated into the Indian Language and delivered accordingly. Furthermore to improve the experience of filling the questionnaire the software supports the integration of translated media like audio and video.
We need some help with user experience testing and would be really obliged if you can spare 15 minutes of your time. We would appreciate if you can ask someone in your family so is not much computer savvy to do it. Please reply to this message if you are interested and we will send you the details.
Please note that the current test checks how well a "layperson" can navigate to the software, find the form to fill and then fill it. Subsequently, a few questions will be asked on how the user felt the software performed and how difficult/easy it was. This user experience testing will essentially allow us to understand the "choke points" where a patient can fail when the software is actually deployed for clinical use.
We will be sending the instructions in a word document if you request for it.
We will acknowledge all those who participate in our system as well as in the initial publication.
Thanks
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Can try
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I'm looking for recommendations on software. I want to have 10-12 undergraduates at my college listen to 18+ one minute audio files of college-level lectures. Learners need to rate each file for comprehensibility and name the main topic in a few words. Then they need to rank the audio files from most to least comprehensible. Because of Covid-19 fears, participants will need to work individually at a computer in a language learning lab with little to no supervision. I want them to be able to enter their responses on the computer, and they need to be able to listen to the audio files as many times as they like, in any order they like. Suggestions??
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Greta: doesn't Texas Tech use Blackboard? If so, that may be the way to go, since your support staff will be able to help you directly in case you have specific questions.
For general info on creating quizzes and tests, go here:
Also, you may want to check out the Respondus Quiz Creator here: https://www.depts.ttu.edu/elearning/blackboard/instructor/respondus/
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I conducted a qualitative research and recorded the interview as audio clips for each respondent. How we analyse the audio based data using R software?
This is quite common in NVIVO and Atlas.ti. but I want to use R software.
Kindly guide me please.
Thanks and regards.
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I tried in the past to use DRAGON software, for interviews in English, and it was a completely disaster. I believe that there are no (good) software for translating recordings due the voices variations.
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For an upcoming research project I will have audio files of interviews conducted in Spanish. I am looking for a company that can transcribe and translate these interviews - resulting in English transcripts. What companies have others used for this? Confidentiality and relatively low cost are important.
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I offer this service. I am a professional translator with 3 years experience translating for a Mexican company and a current MSc student. I have experience with academic articles and publications from English to Spanish (my native language) and viceversa. I have an IELTS certificate (C1) and I am able to offer you affordable and confidential services. Feel free to contact me
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