DeepAI AI Chat
Log In Sign Up

Front-end Diarization for Percussion Separation in Taniavartanam of Carnatic Music Concerts

03/04/2021
by   Nauman Dawalatabad, et al.
0

Instrument separation in an ensemble is a challenging task. In this work, we address the problem of separating the percussive voices in the taniavartanam segments of Carnatic music. In taniavartanam, a number of percussive instruments play together or in tandem. Separation of instruments in regions where only one percussion is present leads to interference and artifacts at the output, as source separation algorithms assume the presence of multiple percussive voices throughout the audio segment. We prevent this by first subjecting the taniavartanam to diarization. This process results in homogeneous clusters consisting of segments of either a single voice or multiple voices. A cluster of segments with multiple voices is identified using the Gaussian mixture model (GMM), which is then subjected to source separation. A deep recurrent neural network (DRNN) based approach is used to separate the multiple instrument segments. The effectiveness of the proposed system is evaluated on a standard Carnatic music dataset. The proposed approach provides close-to-oracle performance for non-overlapping segments and a significant improvement over traditional separation schemes.

READ FULL TEXT
04/08/2020

Conditioned Source Separation for Music Instrument Performances

Separating different music instruments playing the same piece is a chall...
08/03/2020

Multitask learning for instrument activation aware music source separation

Music source separation is a core task in music information retrieval wh...
09/07/2022

Improving Choral Music Separation through Expressive Synthesized Data from Sampled Instruments

Choral music separation refers to the task of extracting tracks of voice...
07/28/2021

Neural Remixer: Learning to Remix Music with Interactive Control

The task of manipulating the level and/or effects of individual instrume...
06/22/2022

Jointist: Joint Learning for Multi-instrument Transcription and Its Applications

In this paper, we introduce Jointist, an instrument-aware multi-instrume...
10/08/2020

All for One and One for All: Improving Music Separation by Bridging Networks

This paper proposes several improvements for music separation with deep ...