Masato Akagi

Kai Li, Khalid Zaman, Xingfeng Li et al.

Early detection of factory machinery malfunctions is crucial in industrial applications. In machine anomalous sound detection (ASD), different machines exhibit unique vibration-frequency ranges based on their physical properties. Meanwhile, the human auditory system is adept at tracking both temporal and spectral dynamics of machine sounds. Consequently, integrating the computational auditory models of the human auditory system with machine-specific properties can be an effective approach to machine ASD. We first quantified the frequency importances of four types of machines using the Fisher ratio (F-ratio). The quantified frequency importances were then used to design machine-specific non-uniform filterbanks (NUFBs), which extract the log non-uniform spectrum (LNS) feature. The designed NUFBs have a narrower bandwidth and higher filter distribution density in frequency regions with relatively high F-ratios. Finally, spectral and temporal modulation representations derived from the LNS feature were proposed. These proposed LNS feature and modulation representations are input into an autoencoder neural-network-based detector for ASD. The quantification results from the training set of the Malfunctioning Industrial Machine Investigation and Inspection dataset with a signal-to-noise (SNR) of 6 dB reveal that the distinguishing information between normal and anomalous sounds of different machines is encoded non-uniformly in the frequency domain. By highlighting these important frequency regions using NUFBs, the LNS feature can significantly enhance performance using the metric of AUC (area under the receiver operating characteristic curve) under various SNR conditions. Furthermore, modulation representations can further improve performance. Specifically, temporal modulation is effective for fans, pumps, and sliders, while spectral modulation is particularly effective for valves.

Bagus Tris Atmaja, Masato Akagi

Silence is a part of human-to-human communication, which can be a clue for human emotion perception. For automatic emotion recognition by a computer, it is not clear whether silence is useful to determine human emotion within a speech. This paper presents an investigation of the effect of using silence feature in dimensional emotion recognition. Since the silence feature is extracted per utterance, we grouped the silence feature with high statistical functions from a set of acoustic features. The result reveals that the silence features affect the arousal dimension more than other emotion dimensions. The proper choice of a threshold factor in the calculation of silence feature improved the performance of dimensional speech emotion recognition performance, in terms of a concordance correlation coefficient. On the other side, improper choice of that factor leads to a decrease in performance by using the same architecture.