Yeshwant Singh


2021

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Multitask Learning based Deep Learning Model for Music Artist and Language Recognition
Yeshwant Singh | Anupam Biswas
Proceedings of the Workshop on Speech and Music Processing 2021

Artist and music language recognitions of music recordings are crucial tasks in the music information retrieval domain. These tasks have many industrial applications and become much important with the advent of music streaming platforms. This work proposed a multitask learning-based deep learning model that leverages the shared latent representation between these two related tasks. Experimentally, we observe that applying multitask learning over a simple few blocks of a convolutional neural network-based model pays off with improvement in the performance. We conduct experiments on a regional music dataset curated for this task and released for others. Results show improvement up to 8.7 percent in AUC-PR, similar improvements observed in AUC-ROC.

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Comparative Analysis of Melodia and Time-Domain Adaptive Filtering based Model for Melody Extraction from Polyphonic Music
Ranjeet Kumar | Anupam Biswas | Pinki Roy | Yeshwant Singh
Proceedings of the Workshop on Speech and Music Processing 2021

Among the many applications of Music Information Retrieval (MIR), melody extraction is one of the most essential. It has risen to the top of the list of current research challenges in the field of MIR applications. We now need new means of defining, indexing, finding, and interacting with musical information, given the tremendous amount of music available at our fingertips. This article looked at some of the approaches that open the door to a broad variety of applications, such as automatically predicting the pitch sequence of a melody straight from the audio signal of a polyphonic music recording, commonly known as melody extraction. It is pretty easy for humans to identify the pitch of a melody, but doing so on an automated basis is very difficult and time-consuming. In this article, a comparison is made between the performance of the currently available melody extraction approach that is state-of-the-art Melodia and the technique based on time-domain adaptive filtering for melody extraction in terms of evaluation metrics introduced in MIREX 2005. Motivating by the same, this paper focuses on the discussion of datasets and state-of-the-art approaches for the extraction of the main melody from music signals. Additionally, a summary of the evaluation matrices based on which methodologies have been examined on various datasets is also present in this paper.