Share This Author
librosa: Audio and Music Signal Analysis in Python
A brief overview of the librosa library's functionality is provided, along with explanations of the design goals, software development practices, and notational conventions.
MIR_EVAL: A Transparent Implementation of Common MIR Metrics
Central to the field of MIR research is the evaluation of algorithms used to extract information from music data. We present mir_eval, an open source software library which provides a transparent and…
End-to-end Learning for Music Audio Tagging at Scale
- Jordi Pons, Oriol Nieto, Matthew Prockup, Erik M. Schmidt, A. Ehmann, X. Serra
- Computer ScienceISMIR
- 7 November 2017
This work focuses on studying how waveform-based models outperform spectrogram-based ones in large-scale data scenarios when datasets of variable size are available for training, suggesting that music domain assumptions are relevant when not enough training data are available.
A Deep Multimodal Approach for Cold-start Music Recommendation
This work addresses the so-called cold-start problem by combining text and audio information with user feedback data using deep network architectures and suggests that both splitting the recommendation problem between feature levels, and merging feature embeddings in a multimodal approach improve the accuracy of the recommendations.
Multi-Label Music Genre Classification from Audio, Text and Images Using Deep Features
Mu, a new dataset of more than 31k albums classified into 250 genre classes, is presented and an approach for multi-label genre classification based on the combination of feature embeddings learned with state-of-the-art deep learning methodologies is proposed.
Data-Driven Harmonic Filters for Audio Representation Learning
- Minz Won, Sanghyuk Chun, Oriol Nieto, X. Serra
- Computer ScienceICASSP - IEEE International Conference on…
- 1 May 2020
Experimental results show that a simple convolutional neural network back-end with the proposed front-end outperforms state-of-the-art baseline methods in automatic music tagging, keyword spotting, and sound event tagging tasks.
Data Driven and Discriminative Projections for Large-Scale Cover Song Identification
This work improves upon previous work in large-scale cover song identification by using data-driven projections at different time-scales to capture local features and embed summary vectors into a semantically organized space.
JAMS: A JSON Annotated Music Specification for Reproducible MIR Research
- Eric J. Humphrey, J. Salamon, Oriol Nieto, Jonathan P. Forsyth, Rachel M. Bittner, J. Bello
- Computer ScienceISMIR
JAMS, a JSON-based music annotation format capable of addressing the evolving research requirements of the community, is proposed, designed to support existing data while encouraging the transition to more consistent, comprehensive, well-documented annotations.
Systematic Exploration of Computational Music Structure Research
In this work we present a framework containing open source implementations of multiple music structural segmentation algorithms and employ it to explore the hyper parameters of features, algorithms,…
Convex non-negative matrix factorization for automatic music structure identification
We propose a novel and fast approach to discover structure in western popular music by using a specific type of matrix factorization that adds a convex constrain to obtain a decomposition that can be…