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4597b03873
Opens in Android Studio but haven't even tried to build it yet (it won't.. I know that much!)
93 lines
4.5 KiB
Text
93 lines
4.5 KiB
Text
HRTF Support
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============
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Starting with OpenAL Soft 1.14, HRTFs can be used to enable enhanced
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spatialization for both 3D (mono) and multi-channel sources, when used with
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headphones/stereo output. This can be enabled using the 'hrtf' config option.
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For multi-channel sources this creates a virtual speaker effect, making it
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sound as if speakers provide a discrete position for each channel around the
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listener. For mono sources this provides much more versatility in the perceived
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placement of sounds, making it seem as though they are coming from all around,
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including above and below the listener, instead of just to the front, back, and
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sides.
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The built-in data set is based on the KEMAR HRTF diffuse data provided by MIT,
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which can be found at <http://sound.media.mit.edu/resources/KEMAR.html>. It's
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only available when using 44100hz playback.
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External HRTF Data Sets
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=======================
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OpenAL Soft also provides an option to use user-specified data sets, in
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addition to or in place of the built-in set. This allows users to provide their
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own data sets, which could be better suited for their heads, or to work with
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stereo speakers instead of headphones, or to support more playback sample
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rates, for example.
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The file format for the data sets is specified below. It uses little-endian
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byte order. Certain data fields are restricted to specific values (these
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restriction may be lifted in future versions of the lib).
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==
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ALchar magic[8] = "MinPHR00";
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ALuint sampleRate;
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ALushort hrirCount; /* Required value: 828 */
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ALushort hrirSize; /* Required value: 32 */
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ALubyte evCount; /* Required value: 19 */
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ALushort evOffset[evCount]; /* Required values:
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{ 0, 1, 13, 37, 73, 118, 174, 234, 306, 378, 450, 522, 594, 654, 710, 755,
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791, 815, 827 } */
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ALshort coefficients[hrirCount][hrirSize];
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ALubyte delays[hrirCount]; /* Element values must not exceed 127 */
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==
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The data is described as thus:
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The file first starts with the 8-byte marker, "MinPHR00", to identify it as an
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HRTF data set. This is followed by an unsigned 32-bit integer, specifying the
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sample rate the data set is designed for (OpenAL Soft will not use it if the
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output device's playback rate doesn't match).
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Afterward, an unsigned 16-bit integer specifies the total number of HRIR sets
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(each HRIR set is a collection of impulse responses forming the coefficients
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for a convolution filter). The next unsigned 16-bit integer specifies how many
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samples are in each HRIR set (the number of coefficients in the filter). The
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following unsigned 8-bit integer specifies the number of elevations used by the
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data set. The elevations start at the bottom, and increment upwards.
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Following this is an array of unsigned 16-bit integers, one for each elevation
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which specifies the index offset to the start of the HRIR sets for each given
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elevation (the number of HRIR sets at each elevation is infered by the offset
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to the next elevation, or by the total count for the last elevation).
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The actual coefficients follow. Each coefficient is a signed 16-bit sample,
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with each HRIR set being a consecutive number of samples. For each elevation,
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the HRIR sets first start with a neutral "in-front" set (that is, one that is
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applied equally to the left and right outputs). After this, the sets follow a
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clockwise pattern, constructing a full circle for the left ear only. The right
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ear uses the same sets but in reverse (ie, left = angle, right = 360-angle).
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After the coefficients is an array of unsigned 8-bit delay values, one for each
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HRIR set. This is the delay, in samples, after recieving an input sample before
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before it's added in to the convolution filter that the corresponding HRIR set
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operates on and gets heard.
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Note that the HRTF data is expected to be minimum-phase reconstructed. The
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time delays are handled by OpenAL Soft according to the specified delay[]
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values, and afterward the samples are fed into the convolution filter using the
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corresponding coefficients. This allows for less processing by using a shorter
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convolution filter, as it skips the first coefficients that do little more than
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cause a timed delay, as well as the tailing coefficients that are used to
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equalize the length of all the sets and contribute nothing.
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For reference, the built-in data set uses a 32-sample convolution filter while
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even the smallest data set provided by MIT used a 128-sample filter (a 4x
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reduction by applying minimum-phase reconstruction). Theoretically, one could
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further reduce the minimum-phase version down to a 16-sample convolution filter
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with little quality loss.
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