I understand how to read 8-bit, 16-bit & 32-bit samples (PCM & floating-point) from a .wav
file, since (conveniently) the .Net Framework has an in-built integral type for those exact sizes. But, I don't know how to read (and store) 24-bit (3 byte) samples.
How can I read 24-bit audio? Is there maybe some way I can alter my current method (below) for reading 32-bit audio to solve my problem?
private List<float> Read32BitSamples(FileStream stream, int sampleStartIndex, int sampleEndIndex)
{
var samples = new List<float>();
var bytes = ReadChannelBytes(stream, Channels.Left, sampleStartIndex, sampleEndIndex); // Reads bytes of a single channel.
if (audioFormat == WavFormat.PCM) // audioFormat determines whether to process sample bytes as PCM or floating point.
{
for (var i = 0; i < bytes.Length / 4; i++)
{
samples.Add(BitConverter.ToInt32(bytes, i * 4) / 2147483648f);
}
}
else
{
for (var i = 0; i < bytes.Length / 4; i++)
{
samples.Add(BitConverter.ToSingle(bytes, i * 4));
}
}
return samples;
}
Reading (and storing) 24-bit samples is very simple. Now, as you've rightly said, a 3 byte integral type does not exist within the framework, which means you're left with two choices; either create your own type, or, you can pad your 24-bit samples by inserting an empty byte (0
) to the start of your sample's byte array therefore making them 32-bit samples (so you can then use an int
to store/manipulate them).
I will explain and demonstrate how to do the later (which is also in my opinion the more simpler approach).
First we must look at how a 24-bit sample would be stored within an int
,
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ MSB ~ ~ 2ndMSB ~ ~ 2ndLSB ~ ~ LSB ~ ~
24-bit sample:
11001101 01101001 01011100 00000000
32-bit sample:
11001101 01101001 01011100 00101001
MSB = Most Significant Byte, LSB = Lest Significant Byte.
As you can see the LSB of the 24-bit sample is 0
, therefore all you have to is declare a byte[]
with 4
elements, then read the 3 bytes of the sample into the array (starting at element 1
) so that your array looks like below (effectively bit shifting by 8 places to the left),
myArray[0]:
00000000
myArray[1]:
01011100
myArray[2]:
01101001
myArray[3]:
11001101
Once you have your byte array full you can pass it to BitConverter.ToInt32(myArray, 0);
, you will then need to shift the sample by 8
places to the right to get the sample in it's proper 24-bit intergal representation (from -8388608
to 8388608
); then divide by 8388608
to have it as a floating-point value.
So, putting that all together you should end up with something like this,
Note, I wrote the following code with the intention to be "easy-to-follow", therefore this will not be the most performant method, for a faster solution see the code below this one.
private List<float> Read24BitSamples(FileStream stream, int startIndex, int endIndex)
{
var samples = new List<float>();
var bytes = ReadChannelBytes(stream, Channels.Left, startIndex, endIndex);
var temp = new List<byte>();
var paddedBytes = new byte[bytes.Length / 3 * 4];
// Right align our samples to 32-bit (effectively bit shifting 8 places to the left).
for (var i = 0; i < bytes.Length; i += 3)
{
temp.Add(0); // LSB
temp.Add(bytes[i]); // 2nd LSB
temp.Add(bytes[i + 1]); // 2nd MSB
temp.Add(bytes[i + 2]); // MSB
}
// BitConverter requires collection to be an array.
paddedBytes = temp.ToArray();
temp = null;
bytes = null;
for (var i = 0; i < paddedBytes.Length / 4; i++)
{
samples.Add(BitConverter.ToInt32(paddedBytes, i * 4) / 2147483648f); // Skip the bit shift and just divide, since our sample has been "shited" 8 places to the right we need to divide by 2147483648, not 8388608.
}
return samples;
}
For a faster1 implementation you can do the following instead,
private List<float> Read24BitSamples(FileStream stream, int startIndex, int endIndex)
{
var bytes = ReadChannelBytes(stream, Channels.Left, startIndex, endIndex);
var samples = new float[bytes.Length / 3];
for (var i = 0; i < bytes.Length; i += 3)
{
samples[i / 3] = (bytes[i] << 8 | bytes[i + 1] << 16 | bytes[i + 2] << 24) / 2147483648f;
}
return samples.ToList();
}
1 After benchmarking the above code against the previous method, this solution is approximately 450% to 550% faster.
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