Papers

For citiation records of the following articles refer to my GoogleScholar page.

Sound Event Localization, Detection, and Tracking by Deep Neural Networks

Sharath Adavanne

Ph.D. Thesis, Tampere University, Finland 2020

Acoustic Scene Classification Using Higher-Order Ambisonic Features

Marc C Green, Sharath Adavanne, Damian Murphy, Tuomas Virtanen

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2019)

Joint Measurement of Localization and Detection of Sound Events

Annamaria Mesaros, Sharath Adavanne, Archontis Politis, Toni Heittola, Tuomas Virtanen

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2019)

A multi-room reverberant dataset for sound event localization and detection

Sharath Adavanne, Archontis Politis, Tuomas Virtanen

Detection and Classification of Acoustic Scenes and Events (DCASE 2019)

Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network

Sharath Adavanne, Archontis Politis, Tuomas Virtanen

Detection and Classification of Acoustic Scenes and Events (DCASE 2019)

Sound Event Classification and Detection with Weakly Labeled Data

Sharath Adavanne, Haytham Fayek, Vladimir Tourbabin

Detection and Classification of Acoustic Scenes and Events (DCASE 2019)

Sound event localization and detection of overlapping sources using convolutional recurrent neural network

Sharath Adavanne, Archontis Politis, Joonas Nikunen and Tuomas Virtanen

IEEE Journal of Selected Topics in Signal Processing (JSTSP 2018) [Slides] [Code]

Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network

Sharath Adavanne*, Archontis Politis* and Tuomas Virtanen

European Signal Processing Conference (EUSIPCO 2018)

Multichannel sound event detection using 3D convolutional neural networks for learning inter-channel features

Sharath Adavanne, Archontis Politis and Tuomas Virtanen

International Joint Conference on Neural Networks (IJCNN 2018) [Slides]

Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network

Sharath Adavanne and Tuomas Virtanen

Detection and Classification of Acoustic Scenes and Events (DCASE 2017) [Presentation]

Automated audio captioning with recurrent neural networks

Konstantinos Drossos*, Sharath Adavanne* and Tuomas Virtanen

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017) [Online demo]

Stacked convolutional and recurrent neural networks for bird audio detection

Sharath Adavanne, Konstantinos Drossos, Emre Cakir and Tuomas Virtanen

European Signal Processing Conference (EUSIPCO 2017)

Convolutional recurrent neural networks for bird audio detection

Emre Cakir, Sharath Adavanne, Giambattista Parascandolo, Konstantinos Drossos and Tuomas Virtanen

European Signal Processing Conference (EUSIPCO 2017)

Stacked convolutional and recurrent neural networks for music emotion recognition

Miroslav Malik*, Sharath Adavanne*, Konstantinos Drossos, Tuomas Virtanen, Dasa Ticha, Roman Jarina

Sound and Music Computing Conference (SMC 2017)

Assessment of support vector machines and convolutional neural networks to detect snoring using Emfit mattress

Jose M. Perez-Macias, Sharath Adavanne, Jari Viik, Alpo Värri, Sari-Leena Himanen, and Mirja Tenhunen

IEEE The Engineering in Medicine and Biology Conference (EMBC 2017)

Sound event detection using spatial features and convolutional recurrent neural network

Sharath Adavanne, Pasi Pertila and Tuomas Virtanen

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) [Code]

Sound event detection in multichannel audio using spatial and harmonic features

Sharath Adavanne, Giambattista Parascandolo, Pasi Pertila, Toni Heittola and Tuomas Virtanen

Detection and Classification of Acoustic Scenes and Events (DCASE 2016)

Room surface estimation using reflection coefficients measured in-situ

Sharath Adavanne

MSc thesis, Tampere University of Technology, Finland 2011

* Equally contributing authors in the paper