M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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    Environmental Sound Classification (ESC) using Handcrafted and Learned Features
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2017) Agrawal, Dharmeshkumar Maheshchandra; Patil, Hemant A.
    "Environmental Sound Classification (ESC) is an important research field due to its application in various field such as hearing aids, road surveillance system for security and safety purpose, etc. ESC task was earlier done using Coefficients (MFCC) feature set and Gaussian Mixture Model (GMM) classifier. Recently, deep-learning based approaches are used for ESC task such as Convolutional Neural Network (CNN) based classification which built an end-to-end system for ESC on CNN framework. The ESC task is a quite challenging problem as of environmental sounds that contains the various categories of sounds are difficult to classify. In this thesis, we proposed two new and different feature sets for ESC task, namely, handcrafted feature set (i.e., signal processing-based approach), and data-driven feature set (i.e., machine learning-based approach). In handcrafted feature set, we propose to use modified Gammatone filterbank with Teager Energy Operator (TEO) for ESC task. In this thesis, we have used two classifiers, namely, GMM using cepstral features, and CNN using spectral features. We performed experiments on two datasets, namely, ESC-50, and UrbanSound8K. We compared TEO-based coefficients with MFCC and Gammatone cepstral coefficients (GTCC), in which GTCC used mean square energy. The result shows score-level fusion of proposed TEO-based Gammatone feature-set and MFCC gave better performance than MFCC on both datasets by using GMM and CNN classifiers. This shows that proposed TEO-based Gammatone features contain complementary information, which is helpful in ESC task. In data-driven feature set, we use Convolutional Restricted Boltzmann Machine (ConvRBM) to learn filterbank from the raw audio signals. ConvRBM is a generative model trained in an unsupervised way to model the audio signals of arbitrary lengths. ConvRBM is trained using annealed dropout technique and parameters are optimized using Adam optimization. The subband filters of ConvRBM learned from the ESC-50 database resemble Fourier basis in the mid-frequency range, while some of the low frequency subband filters resemble Gammatone basis. We have used our proposed model as a front-end for the ESC task with supervised CNN as a back-end."
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    Person recognition from their hum
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Madhavi, Maulik C.; Patil, Hemant A.
    In this thesis, design of person recognition system based on person's hum is presented. As hum is nasalized sound and LP (Linear Predication) model does not characterize nasal sounds sufficiently, our approach in this work is based on using Mel filterbank-based cepstral features for person recognition task. The first task was consisted of data collection and corpus design procedure for humming. For this purpose, humming for old Hindi songs from around 170 subjects are used. Then feature extraction schemes were developed. Mel filterbank follows the human perception for hearing, so MFCC was used as state-of- the-art feature set. Then some modifications in filterbank structure were done in order to compute Gaussian Mel scalebased MFCC (GMFCC) and Inverse Mel scale-based MFCC (IMFCC) feature sets. In this thesis mainly two features are explored. First feature set captures the phase information via MFCC utilizing VTEO (Variable length Teager Energy Operator) in time-domain, i.e., MFCC-VTMP and second captures the vocal-source information called as Variable length Teager Energy Operator based MFCC, i.e., VTMFCC. The proposed feature set MFCCVTMP has two characteristics, viz., it captures phase information and other it uses the property of VTEO. VTEO is extension of TEO and it is a nonlinear energy tracking operator. Feature sets like VTMFCC captures the vocal-source information. This information exhibits the excitation mechanism in the speech (hum) production process. It is found to be having complementary nature of information than the vocal tract information. So the score-level fusion based approach of different source and system features improves the person recognition performance.
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    Speech driven facial animation system
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2006) Singh, Archana; Jotwani, Naresh D.
    This thesis is concerned with the problem of synthesizing animating face driven by new audio sequence, which is not present in the previously recorded database. The main focus of the thesis is on exploring the efficient mapping of the features of speech domain to video domain. The mapping algorithms consist of two parts: building a model to fit the training data set and predicting the visual motion with the novel audio stimuli. The motivation was to construct the direct mapping mechanism from acoustic signals at low levels to visual frames. Unlike the previous efforts at higher acoustic levels (phonemes or words), the current approach skips the audio recognition phase, in which it is difficult to obtain high recognition accuracy due to speaker and language variability.