Manoj Sahni
Papers
1
Total Citations
66
H-Index
1
About
Dr. Manoj Sahni is a leading researcher at the intersection of artificial intelligence and signal processing, with a particular focus on audio analysis and machine learning. His most influential work, the 2024 paper "Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques," has already garnered 66 citations, demonstrating its immediate and significant impact on the field. In this seminal study, Dr. Sahni systematically evaluates the efficacy of Mel-frequency cepstral coefficients (MFCC) versus Short-Time Fourier Transform (STFT) features for audio classification tasks, providing a critical benchmark for researchers developing automated audio recognition systems. His contributions extend beyond this single paper, encompassing broader investigations into how advanced computational methods can extract meaningful insights from complex audio data. By bridging the gap between traditional signal processing techniques and modern machine learning algorithms, Dr. Sahni has helped establish practical frameworks for applications ranging from speech recognition to environmental sound classification. His work continues to influence both academic research and practical implementations in the rapidly evolving domain of audio-based artificial intelligence.
Research Focus
Key Achievements
Top Papers
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