Monaural Music Source Separation using Convolutional Sparse Coding
We present a comprehensive performance study of a new time-domain approach for estimating the components of an observed monaural audio mixture. Unlike existing time-frequency approaches that use the product of a set of spectral templates and their corresponding activation patterns to approximate the spectrogram of the mixture, the proposed approach uses the sum of a set of convolutions of estimated activations with prelearned dictionary filters to approximate the audio mixture directly in the time domain. The approximation problem can be solved by an efficient convolutional sparse coding algorithm. The effectiveness of this approach for source separation of musical audio has been demonstrated in our prior work, but under rather restricted and controlled conditions, requiring the musical score of the mixture being informed a priori and little mismatch between the dictionary filters and the source signals. In this paper, we report an evaluation that considers wider, and more practical, experimental settings. This includes the use of an audio-based multipitch estimation algorithm to replace the musical score, and an external dataset of audio single notes to construct the dictionary filters. Here, our result shows that the proposed approach remains effective with a larger dictionary, and compares favorably with the state-of-the-art nonnegative matrix factorization approach. However, in the absence of the score and in the case of a small dictionary, our approach may not be better.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- 89233218CNA000001
- OSTI ID:
- 1495128
- Report Number(s):
- LA-UR-15-27928
- Journal Information:
- IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 24, Issue 11; ISSN 2329-9290
- Publisher:
- IEEE - ACMCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Synthesizing the note-specific atoms based on their fundamental frequency, used for single-channel musical source separation
|
journal | January 2019 |
Similar Records
Piano Transcription with Convolutional Sparse Lateral Inhibition
Context-dependent piano music transcription with convolutional sparse coding