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Found 27 papers, 15 papers with code
Structure-informed Positional Encoding for Music Generation
Music generated by deep learning methods often suffers from a lack of coherence and long-term organization.
Singer Identity Representation Learning using Self-Supervised Techniques
Significant strides have been made in creating voice identity representations using speech data.
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.
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
Multiple Choice Learning is a simple framework to tackle multimodal density estimation, using the Winner-Takes-All (WTA) loss for a set of hypotheses.
Transfer Learning and Bias Correction with Pre-trained Audio Embeddings
This approach allows representations derived for one task to be applied to another, and can result in high accuracy with less stringent training data requirements for the downstream task.
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.
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation.
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).
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms
Understanding generalization in modern machine learning settings has been one of the major challenges in statistical learning theory.
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
This paper tackles post-hoc interpretability for audio processing networks.
Unsupervised Music Source Separation Using Differentiable Parametric Source Models
Integrating domain knowledge in the form of source models into a data-driven method leads to high data efficiency: the proposed approach achieves good separation quality even when trained on less than three minutes of audio.
Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework
We present a new probabilistic model to address semi-nonnegative matrix factorization (SNMF), called Skellam-SNMF.
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks
Neural network compression techniques have become increasingly popular as they can drastically reduce the storage and computation requirements for very large networks.
Relative Positional Encoding for Transformers with Linear Complexity
Recent advances in Transformer models allow for unprecedented sequence lengths, due to linear space and time complexity.
VQCPC-GAN: Variable-Length Adversarial Audio Synthesis Using Vector-Quantized Contrastive Predictive Coding
Influenced by the field of Computer Vision, Generative Adversarial Networks (GANs) are often adopted for the audio domain using fixed-size two-dimensional spectrogram representations as the "image data".
Self-Supervised VQ-VAE for One-Shot Music Style Transfer
While several style conversion methods tailored to musical signals have been proposed, most lack the 'one-shot' capability of classical image style transfer algorithms.
Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data
Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match the style of a given example.
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.
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks
This assumption is often made for mathematical convenience, since it enables SGD to be analyzed as a stochastic differential equation (SDE) driven by a Brownian motion.
Supervised Symbolic Music Style Translation Using Synthetic Data
Research on style transfer and domain translation has clearly demonstrated the ability of deep learning-based algorithms to manipulate images in terms of artistic style.
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
We show that the behaviors of the two systems are indeed similar for small step-sizes and we identify how the error depends on the algorithm and problem parameters.
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization
Recent studies on diffusion-based sampling methods have shown that Langevin Monte Carlo (LMC) algorithms can be beneficial for non-convex optimization, and rigorous theoretical guarantees have been proven for both asymptotic and finite-time regimes.
Identify, locate and separate: Audio-visual object extraction in large video collections using weak supervision
We tackle the problem of audiovisual scene analysis for weakly-labeled data.
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
The results support our theory and show that the proposed algorithm provides a significant speedup over the recently proposed synchronous distributed L-BFGS algorithm.
Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events
Audio-visual representation learning is an important task from the perspective of designing machines with the ability to understand complex events.
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
We illustrate our framework on the popular Stochastic Gradient Langevin Dynamics (SGLD) algorithm and propose a novel SG-MCMC algorithm referred to as Stochastic Gradient Richardson-Romberg Langevin Dynamics (SGRRLD).
Stochastic Quasi-Newton Langevin Monte Carlo
These second order methods directly approximate the inverse Hessian by using a limited history of samples and their gradients.
