ENVM-BiLSTM
LSTM-based Model for Noise Level Estimation.
The model was trainned on different spectral and chroma features extracted from the DNLE dataset.
Features:
- 20 MFCC
- 20 delta-delta
- 1 spectral centroid
- 12 chromagram
- 12 chroma energy normalized (CENS)
Model Architecture
Layer Type | Number of Layers |
---|---|
LSTM | 3 |
BiLSTM | 3 |
LSTM | 3 |
Regression | 1 |
Results
The DNLE dataset was split into 90% for training and 10% for testing. The model was trained with an "adam" optimizer, a learning rate of 0.0005, and a batch size of 675.
The model achieved an RMSE of 4.58 on average with a minimum of 0.5 and a maximum of 14.4 (scale of 30.6dBA to 81.3dBA), and a standard deviation of 2.72 on the test dataset.
Citing
If you find this model useful in an academic setting please consider citing: (to be updated)
Contact
Please get in touch for further information: (rafael.zequeira@tu-berlin.de)