What does a audio frame contain?

问题:

Im doing some research on how to compare sound files(wave). Basically i want to compare stored soundfiles (wav) with sound from a microphone. So in the end i would like to pre-store some voice commands of my own and then when Im running my app I would like to compare the pre-stored files with input from the microphone.

My thought was to put in some margin when comparing because saying something two times in a row in the exatly same way would be difficult I guess.

So after some googling i see that python have this module named wave and the Wave_read object. That object has a function named readframes(n):

Reads and returns at most n frames of audio, as a string of bytes.

What does these bytes contain? Im thinking of looping thru the wave files one frame at the time comparing them frame by frame.

回答1:

An audio frame, or sample, contains amplitude (loudness) information at that particular point in time. To produce sound, tens of thousands of frames are played in sequence to produce frequencies.

In the case of CD quality audio or uncompressed wave audio, there are around 44,100 frames/samples per second. Each of those frames contains 16-bits of resolution, allowing for fairly precise representations of the sound levels. Also, because CD audio is stereo, there is actually twice as much information, 16-bits for the left channel, 16-bits for the right.

When you use the sound module in python to get a frame, it will be returned as a series of hexadecimal characters:

• One character for an 8-bit mono signal.
• Two characters for 8-bit stereo.
• Two characters for 16-bit mono.
• Four characters for 16-bit stereo.

In order to convert and compare these values you'll have to first use the python wave module's functions to check the bit depth and number of channels. Otherwise, you'll be comparing mismatched quality settings.

回答2:

A simple byte-by-byte comparison has almost no chance of a successful match, even with some tolerance thrown in. Voice-pattern recognition is a very complex and subtle problem that is still the subject of much research.

回答3:

The first thing you should do is a fourier transformation to transform the data into its frequencies. It is rather complex however. I wouldn't use voice recognition libraries here as it sounds like you don't record voices only. You would then try different time shifts (in case the sounds are not exactly aligned) and use the one that gives you the best similarity - where you have to define a similarity function. Oh and you should normalize both signals (same maximum loudness).

回答4:

I believe the accepted description to be slightly incorrect.

A frame appears to be somewhat like stride in graphics formats. For interleaved stereo @ 16 bits/sample, the frame size is 2*sizeof(short)=4 bytes. For non-interleaved stereo @ 16 bits/sample, the samples of the left channel are all one after another, so the frame size is just sizeof(short).