Analysis of Different Machine and Deep Learning Algorithms for Audio Classification

Due to the numerous applications in areas including voice recognition, music genre classification, ambient sound analysis, and more, the problem of assigning semantic labels to audio signals, known as audio classification. Accurately classifying audio signals into established groups like speech, musical genres, or ambient noises is the aim. In this research we use the k-nearest neighbors (KNN) algorithm to choose the most pertinent audio features and lower the dimensionality of the audio features. Then, we train various CNN models with various activation functions (ReLU, SeLU, and ELU) and evaluate how well they perform in comparison to an LSTM model. On the test set, we assess the models' precision and insight, and we talk about the benefits and drawbacks of each model. Additionally, we demonstrate that while the LSTM model can capture the long-term dependencies and context information of the audio signals, CNN models may train discriminative features that capture the temporal and spectral characteristics of various sound classes. CNN and LSTM have achieved excellent classification accuracy and extraordinary performance in collecting significant audio information by making use of the innate temporal and spectral patterns in audio data.