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Found 21 papers, 11 papers with code
Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures
We validate the retrieval performances of our model using the MUSDB18 and MoisesDB datasets.
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning
In this paper, we propose a novel Self-Supervised-Learning scheme to train rhythm analysis systems and instantiate it for few-shot beat tracking.
Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification
The Prototypical Network (ProtoNet) has emerged as a popular choice in Few-shot Learning (FSL) scenarios due to its remarkable performance and straightforward implementation.
Stem-JEPA: A Joint-Embedding Predictive Architecture for Musical Stem Compatibility Estimation
This paper explores the automated process of determining stem compatibility by identifying audio recordings of single instruments that blend well with a given musical context.
Investigating Design Choices in Joint-Embedding Predictive Architectures for General Audio Representation Learning
This paper addresses the problem of self-supervised general-purpose audio representation learning.
Degradation-Invariant Music Indexing
Finally, anchoring the analysis times on local maxima of a selected onset function, an approximative hashing is done to provide a better tolerance to bit corruptions, and in the same time to make easier the scaling of the method.
Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and Spectral Optimal Transport
In neural audio signal processing, pitch conditioning has been used to enhance the performance of synthesizers.
On the choice of the optimal temporal support for audio classification with Pre-trained embeddings
Choosing the best one for a set of tasks is the subject of many recent publications.
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective
In this paper, we address the problem of pitch estimation using Self Supervised Learning (SSL).
Self-Similarity-Based and Novelty-based loss for music structure analysis
Music Structure Analysis (MSA) is the task aiming at identifying musical segments that compose a music track and possibly label them based on their similarity.
Video-to-Music Recommendation using Temporal Alignment of Segments
We propose a novel approach to significantly improve the system's performance using structure-aware recommendation.
SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss
In this paper, we propose a new paradigm to learn audio features for Music Structure Analysis (MSA).
Exploiting Device and Audio Data to Tag Music with User-Aware Listening Contexts
Namely, we propose a system which can generate a situational playlist for a user at a certain time 1) by leveraging user-aware music autotaggers, and 2) by automatically inferring the user's situation from stream data (e. g. device, network) and user's general profile information (e. g. age).
Deep-Learning Architectures for Multi-Pitch Estimation: Towards Reliable Evaluation
We therefore investigate the influence of dataset splits in the presence of several movements of a work cycle (cross-version evaluation) and propose a best-practice splitting strategy for MusicNet, which weakens the influence of individual test tracks and suppresses overfitting to specific works and recording conditions.
Content based singing voice source separation via strong conditioning using aligned phonemes
These approaches use prior information about the target source to improve separation.
Learning to rank music tracks using triplet loss
These algorithms aim at retrieving a ranked list of music tracks based on their similarity with a target music track.
A Prototypical Triplet Loss for Cover Detection
Automatic cover detection -- the task of finding in a audio dataset all covers of a query track -- has long been a challenging theoretical problem in MIR community.
Cover Detection using Dominant Melody Embeddings
In this work, we propose a neural network architecture that is trained to represent each track as a single embedding vector.
Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separations
The input vector is embedded to obtain the parameters that control Feature-wise Linear Modulation (FiLM) layers.
DALI: a large Dataset of synchronized Audio, LyrIcs and notes, automatically created using teacher-student machine learning paradigm
We start with a set of manual annotations of draft time-aligned lyrics and notes made by non-expert users of Karaoke games.
Single-Channel Blind Source Separation for Singing Voice Detection: A Comparative Study
We propose a novel unsupervised singing voice detection method which use single-channel Blind Audio Source Separation (BASS) algorithm as a preliminary step.
Sound Audio and Speech Processing
