Enhancing the Audio MNIST Classification Process Through the Extraction of Temporal Features Through the Application of 1D-CNN and Attention Mechanisms

The primary goal of this work is to improve Audio MNIST spoken digit categorization. Attention approaches extract and highlight temporal information in audio streams using 1D convolutional neural networks (1D-CNNs). This work processes raw audio waveforms directly, unlike many current algorithms that use 2D representations like Mel Spectrograms or other frequency-domain characteristics for audio classification, especially speech detection. We can better record voice dynamics with higher temporal resolution input features. This method improves voice sequential pattern learning and temporal continuity by employing 1D-CNNs to analyze raw time-domain input. Attention mechanisms allow the model to focus on specific audio signal locations. This attention-based architecture lets the network dynamically select the most informative waveform sections, enhancing the model's capacity to discriminate spoken numbers and voice characteristics. The suggested method can be tested using the Audio MNIST dataset, which contains thirty thousand recordings of spoken numbers from zero to nine from sixty speakers. The proposed 1D-CNN with attention mechanisms is extensively tested against baseline models like typical CNNs trained on spectrograms and CNNs based on raw waveforms without attention. Under noisy or demanding settings when minor temporal factors are crucial for differentiation, classification accuracy improves significantly. The model's performance is tested by employing self-attention and temporal attention layers. These tactics improve model interpretability and generalization to speakers with different accents and languages by emphasizing key input signal elements. This hybrid approach uses 1D-CNNs with attention mechanisms to efficiently extract the temporal features from the audio input. Example tasks include spoken digit recognition, speaker identification, and speech pattern analysis. All signs support this. This method can greatly improve audio classification research, especially in domains where temporal dynamics are crucial.