Publication2024
Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques
The record
- Type
- Publication
- Date
- 1 December 2024
- Level
- National
- Journal
- Discover Internet of Things
- Volume
- 4
- Issue
- 1
- Published
- 1 December 2024
- DOI
- 10.1007/s43926-023-00049-y
- Scopus ID
- 85189483388
- ISSN
- 27307239
- Authors
- 4
- Open access
- Yes
- Citations
- 82
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Audio classification model using MFCC and STFT feature extraction with artificial neural networks achieves 91.41% accuracy for sound detection, advancing machine learning methodology.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Digital signal processing and machine learning for audio analysis exemplifies digital technology and AI innovation.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques”. Kortex.