# Integrating Dilated Convolution into DenseLSTM for Audio Source Separation
## 4 Experiments
### Speech Experiment  
#### 4.1.1 Dataset for Speech Experiment  
For the speech enhancement experiment, we mixed the music and noise signal with the speech signal. We used 115 noises database (DB) [43], ESC-50 [44], and NOISEX-92 [45] as the noise DB, WSJ1 [46] as the speech DB, and MUSDB [47] as the music DB.  
The speech, music, and noise DB is organized, as shown in Table 2. MUSDB is already divided into training, validation, and test datasets, and NOISEX-92 is composed of 6 noise types (babble, destroyer engine, destroyer operation, factory1, factory2, m109). WSJ1, a speech DB, is already divided into the training, validation, and test datasets. For the test dataset of speech DB, the eval93 dataset, which is the \"si_et_h1\" folder, in WSJ1 was used.  
Since the size of the public noise DBs is small, several public noise DBs were used. To cope with various noise types, the deep learning model used 115 noise DB and ESC-50 DB with many noise types for training, while the remaining NOISEX-92 DB was used for the test. Because the public noise DB and music DB have less data than the target speech DB, they are repeatedly mixed. Since speech DB is used only once, the mixed signals are different. Of course, it would be nice if the amount of noise and music DB was as large as the speech DB, but it is not easy to collect noise and music data as much as the speech DB.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**DB**} & \multicolumn{2}{c}{**Noise**} & \multicolumn{1}{c}{**Music**} & **Speech** \\ \cline{2-5}  & **115 Noise** & **ESC-50** & **NOISEX-92** & **MUSDB** & **WSJ1** \\ \hline \multirow{2}{*}{Training} & 2.3 h & \multirow{2}{*}{Not used} & 5 h & 30 h \\  & (165 types) & & (86 songs) & (57 utterances \\  & & & & \(\times\) 284 speakers) \\ \hline \multirow{2}{*}{Validation} & 30 min & \multirow{2}{*}{Not used} & 1 h & 5 h \\  & (165 types) & & (14 songs) & (35 utterances \\  & & & & \(\times\) 60 speakers) \\ \hline \multirow{2}{*}{Test} & \multirow{2}{*}{Not used} & 18 min & 3.5 h & 30 min \\  & & (6 types) & (50 songs) & (284 utterances \\  & & & & \(\times\) 10 speakers) \\ \hline \multirow{2}{*}{Remark} & Used for training & \multirow{2}{*}{Used for test} & Different song for & Independent \\  & and validation & & & each dataset \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset configuration for speech experiment.  
Mixed training, validation, and test datasets are created by using each training, validation, and test datasets of music, noise, and speech databases. The training data mixes the noise and music signals to have a random signal-to-noise ratio (SNR) and signal-to-music ratio (SMR) from \(-10\) to \(20\) dB based on the speech signal. The validation data mixes through the same process as training. To create the test dataset for each SNR and SMR combination, the music is mixed with an SMR of \(-10\), \(-5\), \(0\), \(5\), \(10\), \(20\), and \(30\) dB, and the noise is mixed with an SNR of \(-10\), \(0\), and \(10\) dB. As a result, the total training dataset and validation dataset were \(30\) h and \(5\) h, respectively. In addition, the test dataset for each SNR and SMR combination is \(30\) min.