Unsupervised Shift-invariant Feature Learning from Time-series Data

Unsupervised feature learning is one of the key components of machine learningand articial intelligence. Learning features from high dimensional streaming data isan important and dicult problem which is incorporated with number of challenges.Moreover, feature learning algorithms need to be evaluated and generalized for timeseries with dierent patterns and components. A detailed study is needed to clarifywhen simple algorithms fail to learn features and whether we need more complicatedmethods.In this thesis, we show that the systematic way to learn meaningful featuresfrom time-series is by using convolutional or shift-invariant versions of unsupervisedfeature learning. We experimentally compare the shift-invariant versions of clustering,sparse coding and non-negative matrix factorization algorithms for: reconstruction,noise separation, prediction, classication and simulating auditory lters from acousticsignals. The results show that the most ecient and highly scalable clustering algorithmwith a simple modication in inference and learning phase is able to produce meaningfulresults. Clustering features are also comparable with sparse coding and non-negativematrix factorization in most of the tasks (e.g. classication) and even more successful insome (e.g. prediction). Shift invariant sparse coding is also used on a novel application,inferring hearing loss from speech signal and produced promising results.Performance of algorithms with regard to some important factors such as: timeseries components, number of features and size of receptive eld is also analyzed. Theresults show that there is a signicant positive correlation between performance of clusteringwith degree of trend, frequency skewness, frequency kurtosis and serial correlationof data, whereas, the correlation is negative in the case of dataset average bandwidth.Performance of shift invariant sparse coding is aected by frequency skewness, frequencykurtosis and serial correlation of data. Non-Negative matrix factorization is influenced by data characteristics same as clustering

Identifying hearing loss from learned speech kernels

Does a hearing-impaired individual\u27s speech reflect his hearing loss? To investigate this question, we recorded at least four hours of speech data from each of 29 adult individuals, both male and female, belonging to four classes: 3 normal, and 26 severely-to-profoundly hearing impaired with high, medium or low speech intelligibility. Acoustic kernels were learned for each individual by capturing the distribution of his speech data points represented as 20 ms duration windows. These kernels were evaluated using a set of neurophysiological metrics, namely, distribution of characteristic frequencies, equal loudness contour, bandwidth and Q10 value of tuning curve. It turns out that, for our cohort, a feature vector can be constructed out of four properties of these metrics that would accurately classify hearing-impaired individuals with low intelligible speech from normal ones using a linear classifier. However, the overlap in the feature space between normal and hearing-impaired individuals increases as the speech becomes more intelligible. We conclude that a hearing-impaired individual\u27s speech does reflect his hearing loss provided his loss of hearing has considerably affected the intelligibility of his speech