DepAudioNet: An Efficient Deep Model for Audio based Depression Classification

By Xingchen Ma, Hongyu Yang, Qiang Chen, Di Huang, Yunhong Wang. AVEC@ACM Multimedia, 2016

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3ef71aee72d959306105224db9d0e3c3fd62de51

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. Muhammad Sajjad
2. James R. Glass
3. .. Mustaqeem
4. Soonil Kwon
5. Tuka Al Hanai
6. Marzyeh Ghassemi
7. Yonatan Belinkov
8. Wei-Ning Hsu

In Signal Processing

1. Muhammad Sajjad
2. .. Mustaqeem
3. James R. Glass
4. Soonil Kwon
5. Marzyeh Ghassemi
6. David F. Harwath
7. Wei-Ning Hsu
8. Chia-ying Lee

Citations in the corpus

• Detecting Depression with Audio/Text Sequence Modeling of Interviews (2018, methodology): “used audio to model depression by allowing deep neural networks to learn such associations rather than perform feature engineering [15].”
• Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM (2020, methodology): “The joint approach of CNN-LSTM is presented in [24] to extract the deep salient high-level features from raw speech data using CNN and passed to the LSTM network for capturing the sequential information similar to [25].”