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Found 8 papers, 4 papers with code
Locate This, Not That: Class-Conditioned Sound Event DOA Estimation
Existing systems for sound event localization and detection (SELD) typically operate by estimating a source location for all classes at every time instant.
Solos: A Dataset for Audio-Visual Music Analysis
In this paper, we present a new dataset of music performance videos which can be used for training machine learning methods for multiple tasks such as audio-visual blind source separation and localization, cross-modal correspondences, cross-modal generation and, in general, any audio-visual selfsupervised task.
Audio Source Separation
Audio-Visual Synchronization
+1
Audio and Speech Processing
Databases
Sound
Conditioned Source Separation for Music Instrument Performances
In music source separation, the number of sources may vary for each piece and some of the sources may belong to the same family of instruments, thus sharing timbral characteristics and making the sources more correlated.
Sound Audio and Speech Processing
Vocoder-Based Speech Synthesis from Silent Videos
Both acoustic and visual information influence human perception of speech.
Input complexity and out-of-distribution detection with likelihood-based generative models
Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system.
Ranked #10 on
Anomaly Detection
on Unlabeled CIFAR-10 vs CIFAR-100
A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features
We also propose a technique for measuring the similarity between activation maps and audio features which typically presented in the form of a matrix, such as chromagrams or spectrograms.
End-to-End Sound Source Separation Conditioned On Instrument Labels
Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model?
Timbre Analysis of Music Audio Signals with Convolutional Neural Networks
The focus of this work is to study how to efficiently tailor Convolutional Neural Networks (CNNs) towards learning timbre representations from log-mel magnitude spectrograms.
Sound
