On the Improvement of Singing Voice Separation for Monaural Recordings Using the MIR-1K Dataset

By Chao-Ling Hsu, Jyh-Shing Roger Jang. IEEE Transactions on Audio, Speech, and Language Processing, 2010

294 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f9d36a823a21855cc643bfd0e2752a3eaf0cecd2

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In Signal Processing

Citations in the corpus

Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation (2015, methodology): “We evaluate our proposed system using the MIR-1K dataset [14].”
M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus (2022, methodology): “MIR-1K [15] is also used as the semi-supervised dataset to assist virtual adversarial training.”
DEEP CLUSTERING AND CONVENTIONAL NETWORKS FOR MUSIC SEPARATION: STRONGER TOGETHER (2016, methodology): “Unsupervised methods, such as those based on computational auditory scene analysis (CASA) [4], source/filter modeling [5], or low-rank and sparse modeling [6], have difficulty in capturing the dynamics of the vocals and instruments, while supervised methods, such as those based on nonnegative matrix”
Singing-voice separation from monaural recordings using robust principal component analysis (2012, methodology): “We empirically choose λ1 (also suggested in [12]) and gain = 1 to compare with previous literature on singing-voice separation in terms of GNSDR using the MIR1K dataset [8, 10].”
Singing Voice Separation: A Study on Training Data (2019, methodology): “Indeed, public training datasets have been regularly released (MIR1K [17], MedleyDB [18], DSD100 [19] MUSDB [20]) and used to compare different methods, but they are rather small, and often lack diversity.”
Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus (2021, unknown): “Training TTS and SVS systems both require a significant amount of annotated data[9, 10, 15].”