Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network

By Abdul Malik Badshah, Jamil Ahmad, Nasir Rahim, Sung Wook Baik. International Conference on Platform Technology and Service, 2017

358 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1ddeeb4e837820b83cf58d804526a86975ae8fb6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Erik J. Bekkers
2. David W. Romero
3. J. Tomczak
4. Mark Hoogendoorn
5. Seunghyun Yoon
6. Kyomin Jung
7. Edward Jones
8. Thamer Alhussain

In Experimental and Cognitive Psychology

1. Seunghyun Yoon
2. Edward Jones
3. Syed Asif Ahmad Qadri
4. Taiba Maijd Wani
5. Thamer Alhussain
6. Kyomin Jung
7. Teddy Surya Gunawan
8. Mira Kartiwi

In Signal Processing

1. Mark Hoogendoorn
2. Seunghyun Yoon
3. Edward Jones
4. Syed Asif Ahmad Qadri
5. Taiba Maijd Wani
6. Thamer Alhussain
7. Kyomin Jung
8. Teddy Surya Gunawan

Citations in the corpus

• A Comprehensive Review of Speech Emotion Recognition Systems (2021, methodology): “In [71], deep CNN is utilized for emotion classification.”
• Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, methodology): “In [114], a technique for SER that is based on spectrograms and deep”
• Wavelet Networks: Scale Equivariant Learning From Raw Waveforms (2020, background): “[2] Abdul Malik Badshah, Jamil Ahmad, Nasir Rahim, and Sung Wook Baik.”
• Multimodal Speech Emotion Recognition Using Audio and Text (2018, methodology): “Convolutional neural network (CNN)-based models have been trained on information derived from raw audio signals using spectrograms or audio features such as Mel-frequency cepstral coefficients (MFCCs) and low-level descriptors (LLDs) [16, 17, 18].”