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    <p>I think this is <em>possible</em> using javascript (although maybe not advisable, of course). This article:</p> <p><a href="https://developer.mozilla.org/En/Using_XMLHttpRequest#Handling_binary_data" rel="nofollow noreferrer">https://developer.mozilla.org/En/Using_XMLHttpRequest#Handling_binary_data</a></p> <p>... discusses how to access files as binary data, and once you have the audio file as binary data you could do whatever you like with it (I guess, anyway - I'm not real strong with javascript). With audio files in WAV format, this would be a trivial exercise, since the data is already organized by samples in the time domain. With audio files in a compressed format (like MP3), transforming the compressed data back into time-domain samples would be so insanely difficult to do in javascript that I would found a religion around you if you managed to do it successfully.</p> <p><strong>Update</strong>: after reading your question again, I realized that it might actually be possible to do what you're discussing in javascript, even if the files are in MP3 format and not WAV format. As I understand your question, you're actually just looking to locate points of silence within the audio stream, as opposed to actually stripping out the silent stretches.</p> <p>To locate the silent stretches, you wouldn't necessarily need to convert the frequency-domain data of an MP3 file back into the time-domain of a WAV file. In fact, identifying quiet stretches in audio can actually be done more reliably in the frequency domain than in the time domain. Quiet stretches tend to have a distinctively flat frequency response graph, whereas in the time domain the peak amplitudes of audible speech are sometimes not much higher than the peaks of background noise, especially if auto-leveling is occurring.</p> <p>Analyzing an MP3 file in javascript would be significantly easier if the file were CBR (constant bit rate) instead of VBR (variable bit rate).</p>
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