- Represent the audio signal as a sequence of features, e.g., Mel-frequency cepstral coefficients (MFCCs).
- (I) Develop and evaluate a conventional activity recognition system based on using the Gaussian Mixture Model (GMM) perform the training of the model for each activity with corresponding data.
- (II) Develop and evaluate a GMM-UBM system build a general GMM based on data from all activities and then employ maximum a-posteriori (MAP) adaptation using activity-specific data to obtain the model of each activity.
- (III) Develop and evaluate a GMM-SVM system this is based on representing an utterance of recording as a supervector consisting of the means of the adapted GMM components and then using support vector machine (SVM) for classification.
- (IV) Develop and evaluate an i-vector-based system this is based on using the supervector representation but then transforming this into an i-vector with reduced dimensionality for classification.
- To design and perform experimental evaluations using leave-one-out procedure on a given corpus of audio recordings.
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