Results on the MFCC extraction for improving audio capabilities of TIAGo service robot

Abstract

The purpose of this paper is to obtain through simulations high correct classification rates for isolated audio events detection. To obtain the audio signals, we have used a service robot named TIAGo that simulates scenarios from our everyday life. Mel Frequency Cepstral Coefficients features will be extracted for each audio signal. Then will be classified based on the k-Nearest Neighbors algorithm. To better analyze the performance, besides Mel Frequency Cepstral Coefficients coefficients, 6 more coefficients, non- Mel Frequency Cepstral Coefficients, will be extracted. The number of neighbors for the k-Nearest Neighbors algorithm will vary and also the percent value that represents the number of audio signals used for training or for testing. Simulations will be done also about the metrics and distance. For this, Euclidean and Manhattan metric-distance will be implemented. All these scenarios and combinations of them will be perform through this paper. The highest correct classification rate, 99.27%, is obtained for Mel Frequency Cepstral Coefficients using 70% of input data for training, 5 neighbors and the Euclidean metric.