<html><head><meta http-equiv="Content-Type" content="text/html charset=windows-1252"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><div>On May 2, 2014, at 6:27 PM, J Decker <<a href="mailto:d3ck0r@gmail.com">d3ck0r@gmail.com</a>> wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div dir="ltr">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…</div></blockquote><div><br></div><div>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?</div><div><br></div><div><br></div><div>Thank again.</div><div><br></div><br><blockquote type="cite">
<div class="gmail_extra"><br><br><div class="gmail_quote">On Fri, May 2, 2014 at 5:48 PM, Ricky Huang <span dir="ltr"><<a href="mailto:rhuang.work@gmail.com" target="_blank">rhuang.work@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word">Hello all,<div><br></div><div>I am trying to reproduce the Shazam algorithm as outlined in Avery Wang's paper "An Industrial-Strength Audio Search Algorithm" (<a href="http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf" target="_blank">http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf</a>). 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?)</div>
<div><br></div><div><br></div><div>Any help would be appreciated. Thanks.</div><div><br></div><div><br>
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