On May 9, 2014, at 6:58 PM, J Decker <[email protected]> wrote:

> 
> 
> 
> On Mon, May 5, 2014 at 12:25 PM, Ricky Huang <[email protected]> wrote:
> On May 2, 2014, at 6:27 PM, J Decker <[email protected]> wrote:
> 
>> wouldn't have to be a custom filter, just decompress the media file the 
>> normal way..... could actually probably just use the ffmpeg command line 
>> tool to strip the audio and save it as raw samples, then just read the audio 
>> file directly…
> 
> Thank you for the reply.  Can you clarify it a bit for me: does saving the 
> audio as raw samples using ffmpeg perform the FFT necessary to convert audio 
> to frequency-along-time output?
> 
> 
> No; I guess there is some sort of filter available for that; but I thought 
> that was what you wanted to do ... that is apply your FFT algorithm to data, 
> and just needed the data. 


I was looking at both directions - using FT library for converting raw data, 
and getting precomputed data.  So your answer helped.  Thanks.



>  
> Thank again.
> 
> 
>> 
>> 
>> On Fri, May 2, 2014 at 5:48 PM, Ricky Huang <[email protected]> wrote:
>> Hello all,
>> 
>> I am trying to reproduce the Shazam algorithm as outlined in Avery Wang's 
>> paper "An Industrial-Strength Audio Search Algorithm" 
>> (http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf).  One of the 
>> step in this is to convert the audio to spectrogram and identify the 
>> spectrogram peaks.  I am wondering if building a custom audio-filter for 
>> ffmpeg would be the correct way to go?  If so, does anyone have any pointers 
>> on converting the audio data to spectrogram for me?  (algorithm to use, 
>> things to note, etc?)
>> 
>> 
>> Any help would be appreciated.  Thanks.
>> 
>> 
>> 
>> 
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