No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Dynamic noise reduction algorithm based on time-variety filter
Abstract
In this paper, a new dynamic noise reduction algorithm is proposed based on time-variety filter (TVF), which can be implemented in both time and modified discrete cosine transform (MDCT) domain. In time domain, an IIR filter with changeable bandwidth is for the TVF. In frequency domain, combined with MDCT transform, the noise is suppressed in MDCT domain. A few experiment results reveal that the algorithm retains the noise-suppressing performance with low delay and algorithm complexity.
... On the other hand, Human hearing audible frequency range is 20Hz to 20000Hz. Sound recorded audio signal process frequently experience noise problems or troubles [5]. Voice background recorded noise is an unpleasant sound signal that is joined with voice signal at the time of transmission or at the time of creation of speech signal [6]. ...
Numerous studies have explored speech recognition performance in Smart Home environments. However speech recognition accuracy diminishes when voice commands are captured in noisy areas of the home. This study aims to enhance speech recognition performance in such noisy environments. Instead of relying on remote control signals, a Bluetooth system is employed for short-range wireless communication to identify speech commands. Various sound levels are measured in decibels (dB) at different distances using the Smart Noise Application. A filter algorithm with Adaptive Filtering is used to minimize unwanted noise. The algorithm uses Adaptive Time-Frequency Domain Noise Removal (TFDNR) to mitigate background noise. Overall, the integrated system comprising Smartphone, Bluetooth, Arduino microcontroller, and noise detection software exhibits improved performance compared to previous studies, highlighting its potential for seamless smart home automation.
... Sound generates by the three main processes, they are twisting of nerves, wire beating of membranes or blowing of air through holes but human voice mechanism is di fferent as it comes out in di fferent languages and feelings by control of brain [3,4,5]. Audio signal processing often su ffering from noise trouble [6]. ...
In any signal noise is an undesired quantity, however most of the time every signal get mixed with noise at different levels of their processing and application, due to which the information contained by the signal gets distorted and makes the whole signal redundant.A speech signal is very prominent with acoustical noises like bubble noise, car noise, street noise etc. So for removing the noises researchers have developed various techniques which are called filtering. Basically all the filtering techniques are not suitable for every application,hence based on the type of application some techniques are better than the others. Broadly, the filtering techniques can be classified into two categories i.e. linear filtering and non-linear filtering.In this paper a study is presented on some of the filtering techniques which are based on linear and nonlinear approaches. These techniques includes different adaptive filtering based on algorithm like LMS, NLMS and RLS etc., Kalman filter, ARMA and NARMA time series application for filtering, neural networks combine with fuzzy i.e. ANFIS. This paper also includes the application of various features i.e. MFCC, LPC, PLP and gamma for filtering and recognition.
... However, the audible frequency range for human beings are from 20 Hz to 20 KHz [3]. Audio signal processing often suffers from noise trouble [4]. Speech background noise is an undesired signal which mixed with speech signal at the time of generation of speech signal or at the time of transmission [5]. ...
Speech signal quality and its intelligibility degrade when it is mixed with the noise and it becomes hard to extract the original information contained in the signal. Hence, it is very important to reduce/remove the unwanted noise from the speech signal in order to extract the original information contained in the signal. Based on different noises two kinds of filtering techniques is used for removal of the noise from the speech signal, i.e., if the noise is not time varying means stationary, the filtering technique is called linear filtering and if the noise is time varying means nonstationary then nonlinear filtering technique are used to remove the noise. These two broad categories of filtering include many techniques such as adaptive filtering, Kalman filtering, sub-band filtering, gamma filtering, modified Wiener filtering, etc. This paper presented the linear filtering using the different techniques of adaptive filtering and Kalman filtering and a comparison is evaluated on the performance of the applied filters.
This paper presents system identification-based approach to create a Non-linear Auto-Regressive model with Exogenous (NARX)-based adaptive noise filter to remove noise from recorded audio signals. The NARX model was trained with noisy recorded signal as inputs, and clean signal (from the MP3 audio file) as the output. The system identification process then tries to relate between the input and the output so that the noise component from the input is removed in the output stage. The binary Particle Swarm Optimization (BPSO) algorithm was used to perform model structure selection (selection of input and output lagged signals that best explains the future values of the data). Parameter estimation of the NARX model was done using Householder Transform-based QR factorization. Fitting and residual tests results show that the NARX model was successful in estimating the model, and filtering out noise well.
Based on Wavelet analysis and pattern recognition technologies, the paper investigated the feature analysis and de-noising processing of audio signals within the high noises. The programming framework of audio signal sampling and processing was constructed using TI's TMS320VC5502 Microprocessor, TDS560 simulator and PC. To the questions of data transferring among the system, a solution was implemented based on DMA data transfer, double buffer switched and interrupt response mechanism under the control of DSP/BIOS. The defferent features between audio signals and noises were extracted by the wavelet frequency band threshold de-noising algorithm and pattern recognition technology. Other solutions to the questions of interface configuration between DMA and peripheral equipments, using method of DSP memory, etc. were presented respectively, which gives an efficient iilustration for the research of related problems in the field of digital signal processing.
Spectral subtraction is a popular method for the enhancement of
the quality of speech corrupted by additive noise. Implementations of
spectral subtraction require an available estimate of the corrupting
noise. The spectrum of the noise is usually estimated during a period of
time known a priori to be speech free. This estimate is then assumed to
remain stationary over the entire noisy speech signal. This paper makes
use of a standard spectral subtraction algorithm. However, the method
does not require a noise estimation obtained from a period of time when
speech is known not to exist. Instead, use is made of a continuously
running noise estimation algorithm to track the noise which is input to
the spectral subtraction process. As a result the method is novel in
that it: (1) does not require a known nonspeech interval from which to
determine the noise, and (2) can handle both nonwhite and slowly varying
(relative to the speech) noise in an automatic way. Speech features
which are used to estimate the noise content during speech are the
voiced/unvoiced decision, pitch frequency estimate and the confidence of
these features. Results show that the quality of speech degraded by
nonwhite, nonstationary noise can be improved using spectral subtraction
with the proposed noise estimation algorithm
Here, we propose speech-coding procedures achieving high subjective quality, avoiding speech-specific processing and interframe exploitation. Thus, the scheme is tractable for packet-based voice communication, and has the capability of coding generic audio. The architecture is based on an modified discrete cosine transform (MDCT) representation of the signal, and combines efficient vector quantization (VQ) techniques with psychoacoustic principles. Weighted quantization of MDCT coefficients is performed, using a codebook based on a statistical model of the multidimensional MDCT pdf. The weighting and the codebook are adapted for each frame to account for masking thresholds given by a psychoacoustic analysis. Actual quantization is performed using lattices, thereby, achieving close to rate independent complexity. The result is a coding scheme operational at a range of rates. Here, a particular instance at 16 kbits/s, using a sampling frequency of 8 kHz, is shown to perform better than an LD-CELP operating at the same rate, even though no interframe memory is exploited.
Recent advances in tape transport mechanism, tape head, and headset design have led to significant fidelity improvements of personal portable stereo playback and record/play systems using only two 1.5V battery cells.
Therefore the relationship between the low-pass filter bandwidth and signal level [6] is
- Fmax
Fmax]. Therefore the relationship between the low-pass filter bandwidth and signal level [6] is,
LM1894 Dynamic Noise Reduction System
- National Semiconductor
High Performance Dual Dolby B-type Noise Reduction IC for Voltage Application
- Mahathir Mat
- M Ihsan
- Mohd Yassin
- Azlee Nasir Taib
- Zabidi
- Abu Hasliza
- Hassan
Mahathir Mat, Ihsan M. Yassin, Mohd Nasir Taib, Azlee Zabidi, Hasliza
Abu Hassan, "High Performance Dual Dolby B-type Noise Reduction IC
for Voltage Application", IEEE Transaction on Consumer Electronics,
Vol.CE-29, pp. 267-274, No.3,August 1983
Adaptive filter based on NARX model for recorded audio noise removal
- Mahathir Mat
- M Ihsan
- Mohd Yassin
- Azlee Nasir Taib
- Zabidi
- Abu Hasliza
- Hassan
Mahathir Mat, Ihsan M. Yassin, Mohd Nasir Taib, Azlee Zabidi, Hasliza
Abu Hassan, "Adaptive filter based on NARX model for recorded audio
noise removal", IEEE Control and System Graduate Research
Colloquium, pp. 26-32, 2010