Browsing by Author "Alonso-Jiménez, Pablo"

Sort by: Order: Results:

  • 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 ...
  • 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 ...
  • 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 ...
  • Multilabel prototype generation for data reduction in k-nearest neighbour classification
    Valero-Mas, Jose J.; Gallego, Antonio Javier; Alonso-Jiménez, Pablo; Serra, Xavier (Elsevier, 2023)
    Prototype Generation (PG) methods are typically considered for improving the efficiency of the k-Nearest Neighbour (kNN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of ...
  • MUSAV: a dataset of relative arousal-valence annotations for validation of audio models
    Bogdanov, Dmitry; Lizarraga Seijas, Xavier; Alonso-Jiménez, Pablo; Serra, Xavier (2022-09-27)
    We present MusAV, a new public benchmark dataset for comparative validation of arousal and valence (AV) regression models for audio-based music emotion recognition. To gather the ground truth, we rely on relative ...
  • MusAV: a dataset of relative arousal-valence annotations for validation of audio models
    Bogdanov, Dmitry; Lizarraga Seijas, Xavier; Alonso-Jiménez, Pablo; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2022)
    We present MusAV, a new public benchmark dataset for comparative validation of arousal and valence (AV) regression models for audio-based music emotion recognition. To gather the ground truth, we rely on relative judgments ...
  • Music representation learning based on editorial metadata from discogs
    Alonso-Jiménez, Pablo; Serra, Xavier; Bogdanov, Dmitry (2022-09-22)
    This paper revisits the idea of music representation learning supervised by editorial metadata, contributing to the state of the art in two ways. First, we exploit the public editorial metadata available on Discogs, an ...
  • Music representation learning based on editorial metadata from Discogs
    Alonso-Jiménez, Pablo; Serra, Xavier; Bogdanov, Dmitry (International Society for Music Information Retrieval (ISMIR), 2022)
    This paper revisits the idea of music representation learning supervised by editorial metadata, contributing to the state of the art in two ways. First, we exploit the public editorial metadata available on Discogs, an ...
  • Pre-Training Strategies Using Contrastive Learning and Playlist Information for Music Classification and Similarity
    Alonso-Jiménez, Pablo; Favory, Xavier; Foroughmand, Hadrien; Bourdalas, Grigoris; Serra, Xavier; Lidy, Thomas; Bogdanov, Dmitry (2023-04-25)
    In this work, we investigate an approach that relies on contrastive learning and music metadata as a weak source of supervision to train music representation models. Recent studies show that contrastive learning can be ...
  • Tensorflow audio models in Essentia
    Alonso-Jiménez, Pablo; Bogdanov, Dmitry; Pons Puig, Jordi; Serra, Xavier (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    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, ...