Audio scene recognition using time series analysis

A computer program product for audio scene classification in an information retrieval system, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• generating intermediate audio features from respective segments of an input acoustic time series for a same scene captured by a sensor device;
• and
• classifying, using a nearest neighbor search, the respective segments of the input acoustic time series based on the intermediate audio features to generate a final intermediate feature as a classification for the input acoustic time series, each of the respective segments corresponding to a respective different one of different acoustic windows;
• wherein said generating step comprises:
• learning the intermediate audio features from Multi-Frequency Cepstral Component (MFCC) features extracted from the input acoustic time series;
• dividing the same scene into the different acoustic windows having varying ones of the MFCC features;
• and
• feeding the MFCC features of each of the different acoustic windows into respective LSTM units such that a hidden state of each of the respective LSTM units is passed through an attention layer to identify feature correlations between hidden states at different time steps corresponding to different ones of the different acoustic windows, and
• wherein the method further includes replacing a hardware device monitored by the sensor responsive to the final intermediate feature.

A computer processing system for audio scene classification in an information retrieval system, comprising:

• a memory device for storing program code;
• and
• a hardware processor, operatively coupled to the memory device, for running the program code to
• generate intermediate audio features from respective segments of an input acoustic time series for a same scene captured by a sensor device;
• and
• classify, using a nearest neighbor search, the respective segments of the input acoustic time series based on the intermediate audio features to generate a final intermediate feature as a classification for the input acoustic time series, each of the respective segments corresponding to a respective different one of different acoustic windows;
• wherein the hardware processor runs the program code to generate the intermediate audio features to
• learn the intermediate audio features from Multi-Frequency Cepstral Component (MFCC) features extracted from the input acoustic time series;
• divide the same scene into the different acoustic windows having varying ones of the MFCC features;
• and
• feed the MFCC features of each of the different acoustic windows into respective LSTM units such that a hidden state of each of the respective LSTM units is passed through an attention layer to identify feature correlations between hidden states at different time steps corresponding to different ones of the different acoustic windows, and
• wherein the hardware processor replaces a hardware device monitored by the sensor responsive to the final intermediate feature.