pySpeechRev

This python code performs an efficient speech reverberation starting from a dataset of close-talking speech signals and a collection of acoustic impulse responses.

The reverberated signal y[n] is computed in the following way:

y[n]=x[n] * h[n]

where x[n] is the clean signal and * is the convolutional operator.

The script takes in input the following arguments:

• in_folder: folder where the original close-talk dataset is stored.
• out_folder: folder where the reverberated dataset will be stored.
• list.txt : it is a text file where each row should contain: original_wav_file IR_file.

Before run it, make sure you have all the needed python packages. In particular:

• pysoundfile: pip install pysoundfile
• numpy
• scipy

Example:

python pySpeechRev.py clean_examples/ rev_examples/ list.txt

Note that to have meaningful and realistic results, both the impulse responses and the clean speech signal must be sampled at the same sampling rate (e.g., 16 kHz - 16 kHz).

Reverberated TIMIT

To create a reverberated version of TIMIT do the following steps:

• Make sure you have the TIMIT dataset. If not, it can be downloaded from the LDC website (https://catalog.ldc.upenn.edu/LDC93S1).
• Change lst_TIMIT.txt according to the paths of your TIMIT Dataset
• Run:

python pySpeechRev.py $path_TIMIT  $path_TIMIT_rev lst_TIMIT.txt

The current version of TIMIT has been contaminated with some high-quality impulse responses of the DIRHA-English Dataset [3].

Tested on: Python 2.7, Ubuntu

This code has been used in the following papers (please cite them if you use this code):

[1] M. Ravanelli, P. Svaizer, M. Omologo, "Realistic Multi-Microphone Data Simulation for Distant Speech Recognition", in Proceedings of Interspeech 2016. https://arxiv.org/abs/1711.09470

[2] M. Ravanelli, M. Omologo, "Contaminated speech training methods for robust DNN-HMM distant speech recognition", in Proceedings of INTERSPEECH 2015. https://arxiv.org/abs/1710.03538

[3] M. Ravanelli, M. Omologo, "The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments", in Proceedings of ASRU 2015. https://arxiv.org/abs/1710.02560