Please use this identifier to cite or link to this item: http://hdl.handle.net/10263/7512
Title: Instrument Identification from Mixed Audio Signals
Authors: Bhowmick, Sagnik
Keywords: Mixed Audio Signals
BiLSTM layers
HTDemucs
Open- Unmix
Independent Component Analysis(ICA)
Wave-UNet model
Issue Date: Jun-2024
Publisher: Indian Statistical Institute, Kolkata
Citation: 49p.
Series/Report no.: MTech(CS) Dissertation;22-25
Abstract: Sound source separation has been an active research topic over the years. With the advent of deep learning, there has been many developments in this field. Some early works include the Independent Component Analysis(ICA), the Wave-UNet model with the advent of deep learning. Some recent works include the HTDemucs and Open- Unmix. Here, the work was done on the Open-Unmix architecture. The architecture involves spectrogram calculation using STFT, several Multi layer perceptron layers and three BiLSTM layers with skip connections. A modified form of this architecture was involved in this project where transformer was used. The result showed a slight increase in the SDR levels and reduced training time.
Description: Dissertation under the supervision of Dr. Sarbani Palit
URI: http://hdl.handle.net/10263/7512
Appears in Collections:Dissertations - M Tech (CS)

Files in This Item:
File Description SizeFormat 
Sagnik Bhowmick-Cs2225-MTech2024.pdfDissertations - M Tech (CS)756.99 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.