Faculty Publications

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Publications by NITK Faculty

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    Audio segmentation using a priori information in the context of Karnatic Music
    (Institute of Electrical and Electronics Engineers Inc., 2015) Kalyan, V.A.; Sankaranarayanan, S.; Sumam David, S.
    Karnatic Music (KM) is distinct because of the prevalence of gamaka - embellishments to musical notes in the form of frequency traversals. Another important aspect of KM is that the performance style is mostly extempore. Hence, Music Information Retrieval (MIR) tasks in the context of KM are highly challenging. This paper deals with the task of Audio Segmentation and its application to MIR challenges of KM at various levels. This work presents a method that incorporates a priori knowledge about the music system and the audio track at hand for segmenting the audio into its constituent notes. The method uses amplitude and energy based features to train a neural network and an accuracy of 95.2% has been achieved on KM audio samples. The paper also elucidates the application of the method to important MIR tasks such as Music Transcription and Score-Alignment in the context of KM. © 2015 IEEE.
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    Efficient audio segmentation in soccer videos
    (Institute of Electrical and Electronics Engineers Inc., 2016) Raghuram, M.A.; Chavan, N.R.; Koolagudi, S.G.; Ramteke, P.B.
    Identifying different audio segments in videos is the first step for many important tasks such as event detection and speech transcription. Approaches using Mel-Frequency Cepstral coefficients (MFCCs) with Gaussian mixture models (GMMs) and hidden Markov models (HMMs) perform reasonably well in stationary conditions but do not scale to a broad range of environmental conditions. This paper focuses on the audio segmentation in broadcast soccer videos into audio classes such as Silence, Speech Only, Speech Over Crowd, Crowd Only and Excited, with an alternative feature set which is simplistic as well as robust to changes in the environment conditions. Support Vector Machines (SVMs), Neural Networks and Random Forest are used for the classification. The accuracy achieved with SVMs, Neural Networks and Random Forest are 83.80%, 86.07%, and 88.35% respectively. The proposed features and Random Forest classifier are found to achieve better accuracy compared to the other classifiers. © 2016 IEEE.