18 citations · 69 across the 11 of their papers we have counts for
18 papers
Improving Choral Music Separation through Expressive Synthesized Data from Sampled Instruments
Ke Chen, Hao-Wen Dong, Yi Luo +4
Choral music separation refers to the task of extracting tracks of voice parts (e.g., soprano, alto, tenor, and bass) from mixed audio. The lack of datasets has impeded research on…
TONet: Tone-Octave Network for Singing Melody Extraction from Polyphonic Music
Ke Chen, Shuai Yu, Cheng-i Wang +3
Singing melody extraction is an important problem in the field of music information retrieval. Existing methods typically rely on frequency-domain representations to estimate the s…
HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection
Ke Chen, Xingjian Du, Bilei Zhu +3
Audio classification is an important task of mapping audio samples into their corresponding labels. Recently, the transformer model with self-attention mechanisms has been adopted…
Restoring Eye Contact to the Virtual Classroom with Machine Learning
Ross Greer, Shlomo Dubnov
Nonverbal communication, in particular eye contact, is a critical element of the music classroom, shown to keep students on task, coordinate musical flow, and communicate improvisa…
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition
Eunjeong Koh, Shlomo Dubnov
Emotion is a complicated notion present in music that is hard to capture even with fine-tuned feature engineering. In this paper, we investigate the utility of state-of-the-art pre…
WaveGuard: Understanding and Mitigating Audio Adversarial Examples
Shehzeen Hussain, Paarth Neekhara, Shlomo Dubnov +2
There has been a recent surge in adversarial attacks on deep learning based automatic speech recognition (ASR) systems. These attacks pose new challenges to deep learning security…