AASP-P16.9
AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
Jianwei Yu, Hangting Chen, Yanyao Bian, Xiang Li, Yi Luo, Jinchuan Tian, Mengyang Liu, Jiayi Jiang, Tencent, China; Shuai Wang, Shenzhen Research Institute of Big Data, China
Session:
AASP-P16: Music separation; Audio for multimedia and audio processing systems Poster
Track:
Audio and Acoustic Signal Processing
Location:
Poster Zone 4C
Poster Board PZ-4C.9
Poster Board PZ-4C.9
Presentation Time:
Thu, 18 Apr, 16:30 - 18:30 (UTC +9)
Session Chair:
Jordi Pons, Stability AI
Session AASP-P16
AASP-P16.1: PoP-IDLMA: Product-of-Prior Independent Deeply Learned Matrix Analysis for Multichannel Music Source Separation
Takuya Hasumi, Tomohiko Nakamura, Norihiro Takamune, Hiroshi Saruwatari, The University of Tokyo, Japan; Daichi Kitamura, National Institute of Technology, Kagawa College, Japan; Yu Takahashi, Kazunobu Kondo, Yamaha Corporation, Japan
AASP-P16.2: VIRTUAL BASS ENHANCEMENT VIA MUSIC DEMIXING
Riccardo Giampiccolo, Alessandro Ilic Mezza, Alberto Bernardini, Augusto Sarti, Politecnico di Milano, Italy
AASP-P16.3: MUSIC SOURCE SEPARATION BASED ON A LIGHTWEIGHT DEEP LEARNING FRAMEWORK (DTTNET: DUAL-PATH TFC-TDF UNET)
Junyu Chen, Imperial College London, United Kingdom of Great Britain and Northern Ireland; Susmitha Vekkot, Amrita Vishwa Vidyapeetham, India; Pancham Shukla, Imperial College London, United Kingdom of Great Britain and Northern Ireland
AASP-P16.4: ON THE EFFECT OF DATA-AUGMENTATION ON LOCAL EMBEDDING PROPERTIES IN THE CONTRASTIVE LEARNING OF MUSIC AUDIO REPRESENTATIONS
Matthew McCallum, Matthew Davies, Florian Henkel, Jaehun Kim, Samuel Sandberg, Sirius XM, United States of America
AASP-P16.5: SCNet: Sparse Compression Network for Music Source Separation
Weinan Tong, Jiaxu Zhu, Jun Chen, Tsinghua University, China; Shiyin Kang, Tao Jiang, Yang Li, Skywork AI PTE. LTD., China; Zhiyong Wu, Tsinghua University, China; Helen Meng, The Chinese University of Hong Kong, China
AASP-P16.6: MDX-GAN: ENHANCING PERCEPTUAL QUALITY IN MULTI-CLASS SOURCE SEPARATION VIA ADVERSARIAL TRAINING
Ke Chen, University of California San Diego, United States of America; Jiaqi Su, Zeyu Jin, Adobe Research, United States of America
AASP-P16.7: STEREOPHONIC MUSIC SOURCE SEPARATION WITH SPATIALLY-INFORMED BRIDGING BAND-SPLIT NETWORK
Yichen Yang, Haowen Li, Xianrui Wang, Wen Zhang, Northwestern Polytechnical University, China; Shoji Makino, Waseda University, Japan; Jingdong Chen, Northwestern Polytechnical University, China
AASP-P16.8: VOICE TOXICITY DETECTION USING MULTI-TASK LEARNING
Mahesh Kumar Nandwana, Roblox, United States of America; Yifan He, Carnegie Mellon University, United States of America; Joseph Liu, Xiao Yu, Charles Shang, Eloi Du Bois, Morgan McGuire, Kiran Bhat, Roblox, United States of America
AASP-P16.9: AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data
Jianwei Yu, Hangting Chen, Yanyao Bian, Xiang Li, Yi Luo, Jinchuan Tian, Mengyang Liu, Jiayi Jiang, Tencent, China; Shuai Wang, Shenzhen Research Institute of Big Data, China
AASP-P16.10: AAT: ADAPTING AUDIO TRANSFORMER FOR VARIOUS ACOUSTICS RECOGNITION TASKS
Yun Liang, Hai Lin, Shaojian Qiu, Yihang Zhang, South China Agricultural University, China
AASP-P16.11: Hybrid Packet Loss Concealment for Real-Time Networked Music Applications
Alessandro Mezza, Matteo Amerena, Alberto Bernardini, Augusto Sarti, Politecnico di Milano - Dipartimento di Elettronica, Informazione e Bioingegneria Piazza L. Da Vinci 32 , Milano 20133 Italy
Contacts