Recent studies demonstrate various methods to classify noises present in daily human
activity. Most of these methods utilize multiple audio features that require heavy
computation, which increases the latency. This paper presents a real-time sound classifier based on a smartphone by utilizing only the Mel-frequency Cepstral Coefficient (MFCC) as the feature vector. By relying on this single feature and an augmented audio dataset, this system drastically reduced the computation complexity and achieved 92.06% accuracy. This system utilizes the TarsosDSP library for feature extraction and Convolutional Neural Network – Long-Short Term Memory (CNN-LSTM) for both classification and MFCCs determination. The results show that the developed system can classify the noises with higher accuracy and shorter processing time compared with other architectures. Additionally, this system only takes up 0.03 Watts of power consumption, which makes it suitable for future commercial use.

Recent studies demonstrate various methods to classify noises present in daily human
activity. Most of these methods utilize multiple audio features that require heavy
computation, which increases the latency. This paper presents a real-time sound classifier based on a smartphone by utilizing only the Mel-frequency Cepstral Coefficient (MFCC) as the feature vector. By relying on this single feature and an augmented audio dataset, this system drastically reduced the computation complexity and achieved 92.06% accuracy. This system utilizes the TarsosDSP library for feature extraction and Convolutional Neural Network – Long-Short Term Memory (CNN-LSTM) for both classification and MFCCs determination. The results show that the developed system can classify the noises with higher accuracy and shorter processing time compared with other architectures. Additionally, this system only takes up 0.03 Watts of power consumption, which makes it suitable for future commercial use.

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