Faculty Publications
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Item Choice of a classifier, based on properties of a dataset: case study-speech emotion recognition(Springer New York LLC barbara.b.bertram@gsk.com, 2018) Koolagudi, S.G.; Vishnu Srinivasa Murthy, Y.V.S.; Bhaskar, S.P.In this paper, the process of selecting a classifier based on the properties of dataset is designed since it is very difficult to experiment the data on n—number of classifiers. As a case study speech emotion recognition is considered. Different combinations of spectral and prosodic features relevant to emotions are explored. The best subset of the chosen set of features is recommended for each of the classifiers based on the properties of chosen dataset. Various statistical tests have been used to estimate the properties of dataset. The nature of dataset gives an idea to select the relevant classifier. To make it more precise, three other clustering and classification techniques such as K-means clustering, vector quantization and artificial neural networks are used for experimentation and results are compared with the selected classifier. Prosodic features like pitch, intensity, jitter, shimmer, spectral features such as mel frequency cepstral coefficients (MFCCs) and formants are considered in this work. Statistical parameters of prosody such as minimum, maximum, mean (?) and standard deviation (?) are extracted from speech and combined with basic spectral (MFCCs) features to get better performance. Five basic emotions namely anger, fear, happiness, neutral and sadness are considered. For analysing the performance of different datasets on different classifiers, content and speaker independent emotional data is used, collected from Telugu movies. Mean opinion score of fifty users is collected to label the emotional data. To make it more accurate, one of the benchmark IIT-Kharagpur emotional database is used to generalize the conclusions. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.Item Robust Acoustic Event Classification using Fusion Fisher Vector features(Elsevier Ltd, 2019) Mulimani, M.; Koolagudi, S.G.In this paper, a novel Fusion Fisher Vector (FFV) features are proposed for Acoustic Event Classification (AEC) in the meeting room environments. The monochrome images of a pseudo-color spectrogram of an acoustic event are represented as Fisher vectors. First, irrelevant feature dimensions of each Fisher vector are discarded using Principal Component Analysis (PCA) and then, resulting Fisher vectors are fused to get FFV features. Performance of the FFV features is evaluated on acoustic events of UPC-TALP dataset in clean and different noisy conditions. Results show that proposed FFV features are robust to noise and achieve overall 94.32% recognition accuracy in clean and different noisy conditions. © 2019 Elsevier LtdItem Classification of aspirated and unaspirated sounds in speech using excitation and signal level information(Academic Press, 2020) Ramteke, P.B.; Supanekar, S.; Koolagudi, S.G.In this work, consonant aspiration and unaspiration phenomena are studied. It is known that, pronunciation of aspiration and unaspiration is characterized by the ’puff of air’ released at the place of constriction in the vocal tract also known as burst. Here, properties of the vowel immediately after the burst are studied for characterization of the burst. Excitation source signal estimated from speech as low pass filtered linear prediction residual signal is used for the task. The signal characteristics of parameters such as glottal pulse, duration of open, closed & return phases; slope of open, & return phases; duration of burst; ratio of highest and lowest frame wise energies of signal and voice onset point are explored as features to characterize aspiration and unaspiration. Three datasets namely TIMIT, IIIT Hyderabad Marathi and IIIT Hyderabad Hindi (IIIT-H Indic Speech Databases) are used to verify the proposed approach. Random forest, support vector machine and deep feed forward neural networks (DFFNNs) are used as classifiers to test the effectiveness of the features used for the task. Optimal features are selected for the classification using correlation based feature selection (CFS). From the results, it is observed that the proposed features are efficient in classifying the aspirated and unaspirated consonants. Performance of the proposed features in recognition of aspirated and unaspirated phoneme is also evaluated. IIIT Hyderabad Marathi is considered for the analysis. It is observed that the performance of recognition of aspirated and unaspirated sounds using proposed features is improved in comparison with the MFCCs based phoneme recognition system. © 2020 Elsevier LtdItem Acoustic scene classification using projection Kervolutional neural network(Springer, 2023) Mulimani, M.; Nandi, R.; Koolagudi, S.G.In this paper, a novel Projection Kervolutional Neural Network (ProKNN) is proposed for Acoustic Scene Classification (ASC). ProKNN is a combination of two special filters known as the left and right projection layers and Kervolutional Neural Network (KNN). KNN replaces the linearity of the Convolutional Neural Network (CNN) with a non-linear polynomial kernel. We extend the ProKNN to learn from the features of two channels of audio recordings in the initial stage. The performance of the ProKNN is evaluated on the two publicly available datasets: TUT Urban Acoustic Scenes 2018 and TUT Urban Acoustic Scenes Mobile 2018 development datasets. Results show that the proposed ProKNN outperforms the existing systems with an absolute improvement of accuracy of 8% and 14% on TUT Urban Acoustic Scenes 2018 and TUT Urban Acoustic Scenes Mobile 2018 development datasets respectively, as compared to the baseline model of Detection and Classification of Acoustic Scene and Events (DCASE) - 2018 challenge. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Acoustic Scene Classification using Deep Fisher network(Elsevier Inc., 2023) Venkatesh, S.; Mulimani, M.; Koolagudi, S.G.Acoustic Scene Classification (ASC) is the task of assigning a semantic label to an audio recording, based on the surrounding environment. In this work, a Fisher network is introduced for ASC. The proposed method mimics the working mechanism of a feed-forward Convolutional Neural Network (CNN) where, output of a layer is fed as an input to the succeeding layer. The Fisher network consists of a feature extraction step followed by a Fisher layer. The Fisher layer has three sub-layers, namely, Fisher Vector (FV) encoder, temporal pyramid and normalization layers along with feature reduction layer. Gammatone Time Cepstral Coefficients (GTCCs) and Mel-spectrograms are the features encoded as Fisher vector representation in FV encoder sub-layer. Temporal information of the Fisher vectors is retained using temporal pyramid sub-layer. After temporal pyramids are extracted from Fisher vectors, they are available as a feature vector. Irrelevant dimensions of the temporal pyramids are reduced further using Principal Component Analysis (PCA) in normalization and PCA sub-layers. The proposed model is evaluated on five DCASE datasets, TUT Urban Acoustic Scenes 2018 and Mobile, DCASE 2019 Acoustic Scene Classification Task 1(a) and Task 1(b), TAU Urban Acoustic Scenes 2020 datasets. The overall classification accuracy is 93%, 91%, 92%, 91% and 89% for TUT 2018, TUT Mobile 2018, DCASE Task 1(a) 2019, DCASE Task 1(b) 2019, and TAU Urban Acoustic Scenes 2020 datasets, respectively. The proposed model performed much better than the state-of-the-art ASC systems. © 2023 Elsevier Inc.Item Automatic diagnosis of COVID-19 related respiratory diseases from speech(Springer, 2023) Shekhar, K.; Chittaragi, N.B.; Koolagudi, S.G.In this work, an attempt is made to propose an intelligent and automatic system to recognize COVID-19 related illnesses from mere speech samples by using automatic speech processing techniques. We used a standard crowd-sourced dataset which was collected by the University of Cambridge through a web based application and an android/iPhone app. We worked on cough and breath datasets individually, and also with a combination of both the datasets. We trained the datasets on two sets of features, one consisting of only standard audio features such as spectral and prosodic features and one combining excitation source features with standard audio features extracted, and trained our model on shallow classifiers such as ensemble classifiers and SVM classification methods. Our model has shown better performance on both breath and cough datasets, but the best results in each of the cases was obtained through different combinations of features and classifiers. We got our best result when we used only standard audio features, and combined both cough and breath data. In this case, we achieved an accuracy of 84% and an Area Under Curve (AUC) score of 84%. Intelligent systems have already started to make a mark in medical diagnosis, and this type of study can help better the health system by providing much needed assistance to the health workers. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Bi-level Acoustic Scene Classification Using Lightweight Deep Learning Model(Birkhauser, 2024) Spoorthy, V.; Koolagudi, S.G.Identifying a scene based on the environment in which the related audio is recorded is known as acoustic scene classification (ASC). In this paper, a bi-level light-weight Convolutional Neural Network (CNN)-based model is presented to perform ASC. The proposed approach performs classification in two levels. The scenes are classified into three broad categories in the first level as indoor, outdoor, and transportation scenes. The three classes are further categorized into individual scenes in the second level. The proposed approach is implemented using three features: log Mel band energies, harmonic spectrograms and percussive spectrograms. To perform the classification, three CNN classifiers, namely, MobileNetV2, Squeeze-and-Excitation Net (SENet), and a combination of these two architectures, known as SE-MobileNet are used. The proposed combined model encashes the advantages of both MobileNetV2 and SENet architectures. Extensive experiments are conducted on DCASE 2020 (IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events) Task 1B development and DCASE 2016 ASC datasets. The proposed SE-MobileNet model resulted in a classification accuracy of 96.9% and 86.6% for the first and second levels, respectively, on DCASE 2020 dataset, and 97.6% and 88.4%, respectively, on DCASE 2016 dataset. The proposed model is reported to be better in terms of both complexity and accuracy as compared to the state-of-the-art low-complexity ASC systems. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.