Dmitry Bogdanov

Dmitry Bogdanov
University Pompeu Fabra | UPF · Music Technology Group

PhD

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43
Publications
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1,002
Citations

Publications

Publications (43)
Article
Full-text available
Open-source software libraries have a significant impact on the development of Audio Signal Processing and Music Information Retrieval (MIR) systems. Despite the abundance of such tools, there is a lack of an extensive and easy-to-use reference library for audio feature extraction on Web clients. In this article, we present 'Essentia.js', an open-s...
Article
Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information. Deep learning is commonly used to obtain representations using various sources of information, such as the audio, interactions between users and songs, or associated genre metadata. Recently, contrastive learn...
Preprint
Full-text available
Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information. Deep learning is commonly used to obtain representations using various sources of information, such as the audio, interactions between users and songs, or associated genre metadata. Recently, contrastive learn...
Preprint
Full-text available
One of the main limitations in the field of audio signal processing is the lack of large public datasets with audio representations and high-quality annotations due to restrictions of copyrighted commercial music. We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649,091tracks and 148,826 associated playlists annotated by...
Preprint
Full-text available
Music loops are essential ingredients in electronic music production, and there is a high demand for pre-recorded loops in a variety of styles. Several commercial and community databases have been created to meet this demand, but most are not suitable for research due to their strict licensing. We present the Freesound Loop Dataset (FSLD), a new la...
Conference Paper
Full-text available
We present an online graphical web interface for the exploration and evaluation of embedding and tag spaces of music auto-tagging systems. It allows for quick and qualitative evaluation of individual and pairwise tag predictions as well as visualization of tag and embedding latent spaces (original and with dimensionality reduction). We provide tagg...
Preprint
Full-text available
Recent advances in deep learning accelerated the development of content-based automatic music tagging systems. Music information retrieval (MIR) researchers proposed various architecture designs, mainly based on convolutional neural networks (CNNs), that achieve state-of-the-art results in this multi-label binary classification task. However, due t...
Preprint
Full-text available
Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trained deep learning models, and are designed to offer flexibility of use, easy extensibility, and real-time inference. To show the potential of this new in...
Preprint
Full-text available
Automatic tagging of music is an important research topic in Music Information Retrieval achieved improvements with advances in deep learning. In particular, many state-of-the-art systems use Convolutional Neural Networks and operate on mel-spectrogram representations of the audio. In this paper, we compare commonly used mel-spectrogram representat...
Preprint
Full-text available
Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles. In this on-going work we contribute to this research direction analyzing the impact of collaborative filtering recommendations from the perspective of art...
Preprint
Full-text available
The Spotify Sequential Skip Prediction Challenge focuses on predicting if a track in a session will be skipped by the user or not. In this paper, we describe our approach to this problem and the final system that was submitted to the challenge by our team from the Music Technology Group (MTG) under the name "aferraro". This system consists in combi...
Preprint
Full-text available
There are many offline metrics that can be used as a reference for evaluation and optimization of the performance of recommender systems. Hybrid recommendation approaches are commonly used to improve some of those metrics by combining different systems. In this work we focus on music recommendation and propose a new way to improve recommendations,...
Conference Paper
The ACM RecSys Challenge 2018 focuses on music recommendation in the context of automatic playlist continuation. In this paper, we describe our approach to the problem and the final hybrid system that was submitted to the challenge by our team Cocoplaya. This system consists in combining the recommendations produced by two different models using ra...
Conference Paper
Full-text available
Openly available datasets are a key factor in the advancement of data-driven research approaches, including many of the ones used in sound and music computing. In the last few years, quite a number of new audio datasets have been made available but there are still major shortcomings in many of them to have a significant research impact. Among the c...
Conference Paper
Full-text available
While a vast amount of editorial metadata is being actively gathered and used by music collectors and enthusiasts, it is often neglected by music information retrieval and musicology researchers. In this paper we propose to explore Discogs, one of the largest databases of such data available in the public domain. Our main goal is to show how large-...
Conference Paper
Full-text available
This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal...
Conference Paper
Full-text available
This work describes our contribution to the acoustic scene classification task of the DCASE 2017 challenge. We propose a system that consists of the ensemble of two methods of different nature: a feature engineering approach, where a collection of hand-crafted features is input to a Gradient Boosting Machine, and another approach based on learning...
Conference Paper
Full-text available
Semantic annotations of music collections in digital libraries are important for organization and navigation of the collection. These annotations and their associated metadata are useful in many Music Information Retrieval tasks, and related fields in musicology. Music collections used in research are growing in size, and therefore it is useful to...
Conference Paper
Full-text available
Many studies in music classification are concerned with obtaining the highest possible cross-validation result. However , some studies have noted that cross-validation may be prone to biases and that additional evaluations based on independent out-of-sample data are desirable. In this paper we present a methodology and software tools for cross-coll...
Chapter
This chapter gives an introduction to music recommender systems research. We highlight the distinctive characteristics of music, as compared to other kinds of media. We then provide a literature survey of content-based music recommendation, contextual music recommendation, hybrid methods, and sequential music recommendation, followed by overview of...
Chapter
This chapter gives an introduction to music recommender systems research. We highlight the distinctive characteristics of music, as compared to other kinds of media. We then provide a literature survey of content-based music recommendation, contextual music recommendation, hybrid methods, and sequential music recommendation, followed by overview of...
Conference Paper
Full-text available
We introduce the AcousticBrainz project, an open platform for gathering music information. At its core, Acous-ticBrainz is a database of music descriptors computed from audio recordings using a number of state-of-the-art Music Information Retrieval algorithms. Users run a supplied feature extractor on audio files and upload the analysis results to...
Conference Paper
Full-text available
Music Information Retrieval is largely based on descriptors computed from audio signals, and in many practical applications they are to be computed on music corpora containing audio files encoded in a variety of lossy formats. Such encodings distort the original signal and therefore may affect the computation of descriptors. This raises the questio...
Conference Paper
Full-text available
We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an ex- tensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and...
Conference Paper
Full-text available
We present Essentia 2.0, an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal process- ing blocks, statistical characterization of data, and...
Article
Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user’s musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her...
Conference Paper
Full-text available
The emergence of social tagging websites such as Last.fm has provided new opportunities for learning computational models that automatically tag music. Researchers typically obtain music tags from the Internet and use them to construct machine learning models. Nevertheless, such tags are usually noisy and sparse. In this paper, we present a prelimi...
Conference Paper
Full-text available
In this work we propose a novel approach to music recom-mendation based exclusively on editorial metadata. To this end, we pro-pose to use a public database of music releases Discogs.com, which con-tains extensive information about artists, their releases and record la-bels. We rely on an explicit set of music tracks provided by the user as evidenc...
Article
Full-text available
Measuring music similarity is essential for multimedia retrieval. For music items, this task can be regarded as obtaining a suitable distance measurement between songs defined on a certain feature space. In this paper, we propose three of such distance measures based on the audio content: first, a low-level measure based on tempo-related descriptio...
Conference Paper
Full-text available
The amount of digital music has grown unprecedentedly during the last years and requires the development of effective methods for search and retrieval. In particular, content-based preference elicitation for music recommendation is a challenging problem that is effectively addressed in this paper. We present a system which automatically generates r...
Conference Paper
Full-text available
In this work we consider distance-based approaches to music recommendation, relying on an explicit set of music tracks provided by the user as evidence of his/her music preferences. Firstly, we propose a purely content-based approach, working on low-level (timbral, temporal, and tonal) and inferred high-level semantic descriptions of music. Secondl...
Conference Paper
Full-text available
We report here about our submissions to different music classification tasks for the MIREX 2010 evaluations. These submissions are similar to the ones sent at MIREX 2009 (see [1]), if we look at the classifiers and the main audio features. However we added high-level features (or seman-tic features), based on Support Vector Machine models of curate...
Conference Paper
Full-text available
The music we like (i.e. our musical preferences) encodes and communicates key information about ourselves. Depicting such preferences in a condensed and easily understandable way is very appealing, especially considering the current trends in social network communication. In this paper we propose a method to automatically generate, given a provided...
Conference Paper
Full-text available
Recommending relevant and novel music to a user is one of the central applied problems in music information re-search. In the present work we propose three content-based approaches to this task. Starting from an explicit set of mu-sic tracks provided by the user as evidence of his/her music preferences, we infer high-level semantic descriptors, cov...
Conference Paper
Full-text available
This paper describes our submission for the MIREX 2010 audio music similarity and retrieval task. The submission is similar to the one of our previous systems, sent to MIREX 2009 [1]. This task of audio music similarity can be re-garded as obtaining a suitable distance measurement be-tween songs defined on a certain feature space. We propose a hybr...
Conference Paper
Full-text available
Studying the ways to recommend music to a user is a central task within the music information research commu- nity. From a content-based point of view, this task can be regarded as obtaining a suitable distance measurement be- tween songs defined on a certain feature space. We pro- pose two such distance measures. First, a low-level mea- sure based...
Conference Paper
Full-text available
This paper outlines our submissions to different music clas-sification tasks for the Music Information Retrieval Evalu-ation eXchange (MIREX) 2009. We detail here three dif-ferent algorithms tested in mood and genre classification tasks, and in classical composer identification. These al-gorithms are based on Support Vector Machines, Disjoint Princ...
Conference Paper
Full-text available
This paper describes our submissions for the MIREX 2009 audio music similarity and retrieval task. This task can be regarded as obtaining a suitable distance measurement be- tween songs defined on a certain feature space. First, we propose a high-level semantic measure based on regression by support vector machines of different groups of musical di...

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