Exploration of Machine Learning for Sound and Signal Investigation

Abstract

This study investigates the use of machine learning methods for signal and sound data analysis and interpretation. Its objectives are to create reliable techniques for obtaining features from unprocessed audio signals, find appropriate algorithms for precise classification, regression, and anomaly detection tasks, train models on a variety of datasets, and investigate real-world applications in domains such as speech recognition, noise reduction, and medical diagnosis. Data gathering feature extraction, model selection and training, model evaluation, and practical application are all included in the technique. Future research paths in real-time signal processing and multimodal analysis, practical applications in domains including audio engineering, medical diagnostics, and environmental monitoring, and enhanced machine learning models are among the anticipated results. Speech recognition is crucial for human-computer and human-robot interaction. The Smart-Home Research aims to design a new methodology to reduce speech conversion effort, benefiting visually and physically challenged individuals and those who cannot type. The system uses raw audio signals to extract information from speech and sound, allowing users to connect with relatives, physicians, or caregivers. The study focuses on spotting and Vocabulary Continuous Speech Recognition, using an outsized system to extend robustness and adapt language and acoustic models for multisource-based recognition. This research aims to analyse the challenges of implementing speech recognition technology in various applications, such as classrooms, voice-operated robots, and smart homes. The objectives include evaluating signal performance, analysing speech recognition techniques, identifying problems with SR-mLA, analysing machine learning algorithms, and developing a new framework using the identified efficient algorithms. The research aims to improve pedagogical purposes and enhance lecture transcripts for students. The research evaluates the performance of the speech signal recognition (SR) technique, comparing algorithms like HMM, ANN, PNCC, and DTW. The proposed machine learning framework achieves 98% accuracy for 1-word and 95% for 2-word utterances, but struggles with 2-word recognition due to background noise. Future work includes performance enhancement and multilingual applications.