Audio Signal Processing
17 papers with code • 0 benchmarks • 1 datasets
This is a general task that covers transforming audio inputs into audio outputs, not limited to existing PaperWithCode categories of Source Separation, Denoising, Classification, Recognition, etc.
These leaderboards are used to track progress in Audio Signal Processing
We present a data-driven approach to automate audio signal processing by incorporating stateful third-party, audio effects as layers within a deep neural network.
A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis.
In this work we present a data-driven approach for predicting the behavior of (i. e., profiling) a given non-linear audio signal processing effect (henceforth "audio effect").
Deep neural networks have shown promise for music audio signal processing applications, often surpassing prior approaches, particularly as end-to-end models in the waveform domain.
Disentangling and recovering physical attributes, such as shape and material, from a few waveform examples is a challenging inverse problem in audio signal processing, with numerous applications in musical acoustics as well as structural engineering.
We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649, 091tracks and 148, 826 associated playlists annotated by 30, 652 different tags.
The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD).