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Found 9 papers, 7 papers with code
Jam-ALT: A Formatting-Aware Lyrics Transcription Benchmark
Current automatic lyrics transcription (ALT) benchmarks focus exclusively on word content and ignore the finer nuances of written lyrics including formatting and punctuation, which leads to a potential misalignment with the creative products of musicians and songwriters as well as listeners' experiences.
Ranked #1 on
Automatic Lyrics Transcription
on Jam-ALT Spanish
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.
Music Demixing Challenge 2021
The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i. e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers.
Open-Unmix - A Reference Implementation for Music Source Separation
Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.
Ranked #15 on
Music Source Separation
on MUSDB18
(using extra training data)
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count Estimation
Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance.
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
To the best of our knowledge, the proposed algorithm is the first nonparametric IGM algorithm with explicit theoretical guarantees.
An Overview of Lead and Accompaniment Separation in Music
For model-based methods, we organize them according to whether they concentrate on the lead signal, the accompaniment, or both.
Sound Audio and Speech Processing
The 2018 Signal Separation Evaluation Campaign
This paper reports the organization and results for the 2018 community-based Signal Separation Evaluation Campaign (SiSEC 2018).
Classification vs. Regression in Supervised Learning for Single Channel Speaker Count Estimation
The task of estimating the maximum number of concurrent speakers from single channel mixtures is important for various audio-based applications, such as blind source separation, speaker diarisation, audio surveillance or auditory scene classification.
Audio and Speech Processing Sound
