Visualitza per autoria "Bogdanov, Dmitry"

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  • Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks
    Fonseca, Eduardo; Gong, Rong; Bogdanov, Dmitry; Slizovskaia, Olga; Gómez Gutiérrez, Emilia, 1975-; Serra, Xavier (Tampere University of Technology, 2017)
    This work describes our contribution to the acoustic scene classifi- cation task of the DCASE 2017 challenge. We propose a system that consists of the ensemble of two methods of different nature: a feature engineering ...
  • AcousticBrainz: a community platform for gathering music information obtained from audio
    Porter, Alastair; Bogdanov, Dmitry; Kaye, Robert; Tsukanov, Roman; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2015)
    We introduce the AcousticBrainz project, an open platform for gathering music information. At its core, AcousticBrainz is a database of music descriptors computed from audio recordings using a number of state-of-the-art ...
  • Ambiguity modelling with label distribution learning for music classification
    Buisson, Morgan; Alonso-Jiménez, Pablo; Bogdanov, Dmitry (Institute of Electrical and Electronics Engineers (IEEE), 2022)
    An important amount of work has been devoted to the task of music classification. Despite promising results achieved by convolutional neural networks, there still exists a gap left to be filled for such models to perform ...
  • Artist and style exposure bias in collaborative filtering based music recommendations
    Ferraro, Andrés; Bogdanov, Dmitry; Serra, Xavier; Yoon, Jason (ACM FAT Network, 2019)
    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 ...
  • Artist biases in collaborative filtering for music recommendation
    Ferraro, Andrés; Jeon, Jea Ho; Kim, Biho; Serra, Xavier; Bogdanov, Dmitry (ICML, 2020)
    To evaluate if the recommendations are fair, we have to consider how all the stakeholders are affected. In this work, we focus on the artists in the music domain. We analyze the recommendations made with Collaborative ...
  • Audio and music analysis on the web using Essentia.js
    Correya, Albin Andrew; Marcos Fernández, Jorge; Joglar-Ongay, Luis; Alonso Jiménez, Pablo; Serra, Xavier; Bogdanov, Dmitry (Ubiquity Press, 2021)
    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 ...
  • Automatic detection of audio problems for quality control in digital music distribution
    Alonso-Jiménez, Pablo; Joglar-Ongay, Luis; Serra, Xavier; Bogdanov, Dmitry (Audio Engineering Society, 2019)
    Providing contents within the industry quality standards is crucial for digital music distribution companies. For this reason an excellent quality control (QC) support is paramount to ensure that the music does not contain ...
  • Automatic playlist continuation using a hybrid recommender system combining features from text and audio
    Ferraro, Andrés; Bogdanov, Dmitry; Yoon, Jisang; Kim, KwangSeob; Serra, Xavier (ACM Association for Computer Machinery, 2018)
    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 ...
  • Content-based music recommendation based on user preference examples
    Bogdanov, Dmitry; Haro Berois, Martín; Fuhrmann, Ferdinand; Gómez Gutiérrez, Emilia, 1975-; Herrera Boyer, Perfecto, 1964- (CEUR Workshop Proceedings, 2010)
    Recommending relevant and novel music to a user is one of the central applied problems in music information research. In the present work we propose three content-based approaches to this task. Starting from an explicit ...
  • A Content-based system for music recommendation and visualization of user preferences working on semantic notions
    Bogdanov, Dmitry; Haro Berois, Martín; Fuhrmann, Ferdinand; Xambó, Anna; Gómez Gutiérrez, Emilia, 1975-; Herrera Boyer, Perfecto, 1964- (Institute of Electrical and Electronics Engineers (IEEE), 2011)
    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, contentbased preference elicitation for music recommendation ...
  • Cross-collection evaluation for music classification tasks
    Bogdanov, Dmitry; Porter, Alastair; Herrera Boyer, Perfecto, 1964-; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2016)
    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 ...
  • Deep embeddings with Essentia models
    Alonso-Jiménez, Pablo; Bogdanov, Dmitry; Serra, Xavier (ISMIR, 2020)
    We present the integration of various CNN TensorFlow models developed for different MIR tasks into Essentia. This is a continuation of our previous work [1], extending the list of supported models and adding new algorithms ...
  • Efficient supervised training of audio transformers for music representation learning
    Alonso Jiménez, Pablo; Serra, Xavier; Bogdanov, Dmitry (2023-10-03)
    In this work, we address music representation learning using convolution-free transformers. We build on top of existing spectrogram-based audio transformers such as AST and train our models on a supervised task using ...
  • Enriched music representations with multiple cross-modal contrastive learning
    Ferraro, Andrés; Favory, Xavier; Drossos, Konstantinos; Kim, Yuntae; Bogdanov, Dmitry (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    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 ...
  • Essentia API: a web API for music audio analysis
    Correya, Albin Andrew; Bogdanov, Dmitry; Alonso Jiménez, Pablo; Serra, Xavier (2023-01-10)
    We present Essentia API, a web API to access a collection of state-of-the-art music audio analysis and description algorithms based on Essentia, an open-source library and machine learning (ML) models for audio and music ...
  • Essentia.js: a JavaScript library for music and audio analysis on the web
    Correya, Albin Andrew; Bogdanov, Dmitry; Joglar-Ongay, Luis; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2020)
    Open-source software libraries for audio/music analysis and feature extraction have a significant impact on the development of Audio Signal Processing and Music Information Retrieval (MIR) systems. Despite the abundance of ...
  • Essentia: an audio analysis library for music information retrieval
    Bogdanov, Dmitry; Wack, Nicolas; Gómez Gutiérrez, Emilia, 1975-; Gulati, Sankalp; Herrera Boyer, Perfecto, 1964-; Mayor, Oscar; Roma Trepat, Gerard; Salamon, Justin; Zapata González, José Ricardo; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2013)
    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 ...
  • ESSENTIA: an open source library for audio analysis
    Bogdanov, Dmitry; Wack, Nicolas; Gómez Gutiérrez, Emilia, 1975-; Gulati, Sankalp; Herrera Boyer, Perfecto, 1964-; Mayor, Oscar; Roma Trepat, Gerard; Salamon, Justin; Zapata González, José Ricardo; Serra, Xavier (ACM Association for Computer Machinery, 2014)
    Over the last decade, audio analysis has become afield of active research in academic and engineeringworlds. It refers to the extraction of information andmeaning from audio signals for analysis, ...
  • ESSENTIA: an open-source library for sound and music analysis
    Bogdanov, Dmitry; Wack, Nicolas; Gómez Gutiérrez, Emilia, 1975-; Gulati, Sankalp; Herrera Boyer, Perfecto, 1964-; Mayor, Oscar; Roma Trepat, Gerard; Salamon, Justin; Zapata González, José Ricardo; Serra, Xavier (ACM Association for Computer Machinery, 2013)
    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 ...
  • Exploration of music collections with audio embeddings
    Tovstogan, Philip (Universitat Pompeu Fabra, 2022-07-27)
    Music recommendation systems (RecSys) are integral to modern music streaming services. While there is much research on many aspects of RecSys, there is not enough research on exploration and discovery that contributes to ...

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