Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Manjunath Mulimani"'
Publikováno v:
Multimedia Tools and Applications. 82:9447-9457
Publikováno v:
Digital Signal Processing. 139:104062
Autor:
Shantanu Sachdeva, Manjunath Mulimani
Publikováno v:
2022 9th International Conference on Computing for Sustainable Global Development (INDIACom).
Publikováno v:
Interspeech 2021.
Publikováno v:
Applied Acoustics. 155:130-138
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 vecto
Publikováno v:
Digital Signal Processing. 87:1-9
In this paper, we proposed a novel parallel method for extraction of significant information from spectrograms using MapReduce programming model for the audio-based surveillance system, which effectively recognizes critical acoustic events in the sur
Publikováno v:
Expert Systems with Applications. 120:413-425
The traditional frame-based speech features such as Mel-frequency cepstral coefficients (MFCCs) are specifically developed for speech/speaker recognition tasks. Speech is different from acoustic events, when one considers its phonetic structure. Henc
Publikováno v:
2021 6th International Conference for Convergence in Technology (I2CT).
Enabling devices to make sense of sound is known as Acoustic Scene Classification (ASC). The analysis of various scenes by applying computational algorithms is known as computational auditory scene analysis. The main aim of this paper is to classify
Autor:
Sreetama Mukherjee, Manjunath Mulimani
Publikováno v:
Expert Systems with Applications. 191:116195
Every music composition has a composer at the core of its building block, molding it into a style of their own. The creative compositional style of a composer varies dynamically with every composer which is perishable and inimitable but cannot be pre
Publikováno v:
ICASSP
In this paper, a Deep Neural Network (DNN)-driven feature learning method for polyphonic Acoustic Event Detection (AED) is proposed. The proposed DNN is a combination of different layers used to characterize multiple overlapped acoustic events in the