arXiv subjects

Palaniandavar Venkateswaran

Publications and source records attributed to Palaniandavar Venkateswaran.

4 recordsLinked to original sources

arXiv2023

Exploring Music Genre Classification: Algorithm Analysis and Deployment Architecture

Music genre classification has become increasingly critical with the advent of various streaming applications. Nowadays, we find it impossible to imagine using the artist's name and song title to search for music in a sophisticated music app. It is always difficult to classify music correctly because the information linked to music, such as region, artist, album, or non-album, is so variable. This paper presents a study on music genre classification using a combination of Digital Signal Processing (DSP) and Deep Learning (DL) techniques. A novel algorithm is proposed that utilizes both DSP and DL methods to extract relevant features from audio signals and classify them into various genres. The algorithm was tested on the GTZAN dataset and achieved high accuracy. An end-to-end deployment architecture is also proposed for integration into music-related applications. The performance of the algorithm is analyzed and future directions for improvement are discussed. The proposed DSP and DL-based music genre classification algorithm and deployment architecture demonstrate a promising approach for music genre classification.

cs.SD↗

arXiv2023

Design of a Low-Power High-Gain Bio-Medical Operational Amplifier in 65nm Technology using gm/ID Methodology

Operational Amplifiers (Op-Amps) play a crucial role in the field of biomedical engineering, as they enable signal amplification and processing in various medical devices. With the increasing demand for portable and low-power biomedical devices, designing Op-Amps specifically tailored for such applications is essential. In response to this need, a low-power high-gain Op-Amp designed for biomedical applications using TSMC 65nm technology has been proposed. This Op-Amp incorporates a two-stage miller compensated topology, which is well-known for its superior performance in gain, gain bandwidth product and power consumption. The proposed Op-Amp contributes to the field of biomedical engineering by offering a tailored solution that enhances signal processing capabilities, enables accurate data acquisition, and improves overall efficiency in healthcare systems. The design methodology and simulation results presented in this paper provide insights into the performance and potential impact of the Op-Amp in advancing biomedical devices and systems.

cs.AR↗

arXiv2023

An Improved PMOS-Based Low Dropout Regulator Design for Large Loads

A stable low dropout (LDO) voltage regulator topology is presented in this paper. LDOs are linear voltage regulators that do not produce ripples in the DC voltage. Despite the close proximity of the supply input voltage to the output, this regulator will maintain the desired output voltage. Based on a detailed comparison between NMOS and PMOS-based LDOs, we decided to opt for a PMOS design because it does not require an additional charge pump as compared to NMOS. A demonstration of how Miller capacitance enhances overall design stability is also presented here. Multiple pass elements are arranged in parallel in order to increase the current carrying capacity of the pass network.

cs.AR↗

arXiv2022

Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals

Wireless sensor networks are among the most promising technologies of the current era because of their small size, lower cost, and ease of deployment. With the increasing number of wireless sensors, the probability of generating missing data also rises. This incomplete data could lead to disastrous consequences if used for decision-making. There is rich literature dealing with this problem. However, most approaches show performance degradation when a sizable amount of data is lost. Inspired by the emerging field of graph signal processing, this paper performs a new study of a Sobolev reconstruction algorithm in wireless sensor networks. Experimental comparisons on several publicly available datasets demonstrate that the algorithm surpasses multiple state-of-the-art techniques by a maximum margin of 54%. We further show that this algorithm consistently retrieves the missing data even during massive data loss situations.

eess.SP↗