Please use this identifier to cite or link to this item: http://hdl.handle.net/10263/7512
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dc.contributor.authorBhowmick, Sagnik-
dc.date.accessioned2025-02-07T11:25:16Z-
dc.date.available2025-02-07T11:25:16Z-
dc.date.issued2024-06-
dc.identifier.citation49p.en_US
dc.identifier.urihttp://hdl.handle.net/10263/7512-
dc.descriptionDissertation under the supervision of Dr. Sarbani Paliten_US
dc.description.abstractSound 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.en_US
dc.language.isoenen_US
dc.publisherIndian Statistical Institute, Kolkataen_US
dc.relation.ispartofseriesMTech(CS) Dissertation;22-25-
dc.subjectMixed Audio Signalsen_US
dc.subjectBiLSTM layersen_US
dc.subjectHTDemucsen_US
dc.subjectOpen- Unmixen_US
dc.subjectIndependent Component Analysis(ICA)en_US
dc.subjectWave-UNet modelen_US
dc.titleInstrument Identification from Mixed Audio Signalsen_US
dc.typeOtheren_US
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