activity
20182026
most citedOn The Compensation Between Magnitude and Phase in Speech Separation

104 citations · 305 across the 50 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

eess.AS2023

NeuroHeed+: Improving Neuro-steered Speaker Extraction with Joint Auditory Attention Detection

Zexu Pan, Gordon Wichern, Francois G. Germain +2

Neuro-steered speaker extraction aims to extract the listener's brain-attended speech signal from a multi-talker speech signal, in which the attention is derived from the cortical…

eess.AS2023★ 1 cited

Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction

Zexu Pan, Gordon Wichern, Yoshiki Masuyama +4

Target speech extraction aims to extract, based on a given conditioning cue, a target speech signal that is corrupted by interfering sources, such as noise or competing speakers. B…

eess.AS2023

Generation or Replication: Auscultating Audio Latent Diffusion Models

Dimitrios Bralios, Gordon Wichern, François G. Germain +4

The introduction of audio latent diffusion models possessing the ability to generate realistic sound clips on demand from a text description has the potential to revolutionize how…

cs.SD2023

Improving Audio Captioning Models with Fine-grained Audio Features, Text Embedding Supervision, and LLM Mix-up Augmentation

Shih-Lun Wu, Xuankai Chang, Gordon Wichern +4

Automated audio captioning (AAC) aims to generate informative descriptions for various sounds from nature and/or human activities. In recent years, AAC has quickly attracted resear…

eess.AS2023

The Sound Demixing Challenge 2023 $\unicode{x2013}$ Cinematic Demixing Track

Stefan Uhlich, Giorgio Fabbro, Masato Hirano +14

This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX'23). We provide a comprehensive summary of the challenge setup, detailing the str…

cs.SD2023

Pac-HuBERT: Self-Supervised Music Source Separation via Primitive Auditory Clustering and Hidden-Unit BERT

Ke Chen, Gordon Wichern, François G. Germain +1

In spite of the progress in music source separation research, the small amount of publicly-available clean source data remains a constant limiting factor for performance. Thus, rec…

eess.AS2023★ 29 cited

TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings

Christoph Boeddeker, Aswin Shanmugam Subramanian, Gordon Wichern +2

Since diarization and source separation of meeting data are closely related tasks, we here propose an approach to perform the two objectives jointly. It builds upon the target-spea…