In addition the annoying pops between audio samples proved to be a challange to using tones generated by pyaudio (and by extension 'port audio' which pyaudio uses)for deeplearning. This solution is provided in my dtmf.py script. wav file and as one contiguous float32 bit sample. There were plenty of examples using microphone generated audio samples but not systhesized audio like DTMF tones into a.
Tonegenerator synchronized android java how to#
I could not find any example on the net of how to save a sythesized waveform using the pyaudio module into a.
that the 'wave module' won't support 32 bit samples as float32 and only 16 bit or 24 bit samples. The DTMF tone generator saves all the tones as one long numpy array to be used to save all the captured audio as a single wave file.I have provided a jupyter notebook of the 3 python scripts for easy viewing. In particular it adds some special features to make it useful I think for deeplearning. This DTMF encoder modifies a simple encoder created by Noah Spurrier. On the surface a DTMF audio tone generator would seem a trivial execise, but a number of significant challanges arose using pyaudio as an audio source to create samples for deeplearning. Why DTMF? My reasoning is, its more complex than straight single tones, less complex than voice or music samples, and it perhaps has other uses as well given its orginal use was as a telephony dialer format. The original intent of this project was to create a DTMF (Dual Tone Multi Frequency) tone generator and metadata file to create audio samples for deep learning.
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