Introduction

AudioMark™ is a benchmark which models a sophisticated, real-world audio pipeline that uses a neural net for keyword spotting. EEMBC developed this benchmark in response to the massive proliferation of products utilizing an audio controlled Human-Machine Interface (HMI) that rely on such a pipeline. This includes everything from personal assistants like Alexa, to white-box appliances like washers, dryers, and refrigerators, to home entertainment systems, and even cars that respond to voice commands.

Theory of operation

The benchmark works by processing two microphone inputs listening to both a speaker and reflected noise. A state-of-the-art adaptive beamformer determines the direction of arrival of the primary speaker. This augmented signal is then treated for echo cancellation and noise reduction. The cleaned signal is sent to an MFCC feature extractor which feeds a neural net that spots one of ten known keywords.

The benchmark API facilitates hardware acceleration for key DSP and NN functionality. The file ee_api.h describes the functions that the system integrator must implement. The components were derived from several sources:

The beamformer and direction of arrival algorithms (BF+DOA) were written and tested by Arm and Infineon.
The acoustic echo canceller (AEC) and audio noise reduction (ANR) elements are implemented by the SpeeX libspeexdsp library. These functions utilize the SpeeX API, which is a combination of macros and functions that perform floating-point/fixed math operations, and an FFT wrapper for transformation. In AudioMark, only the single precision floating-point version of the library is used.
The neural net was derived from the Arm Model Zoo DS CNN KWS.

This DS-CNN model was chosen for its flexibility of utilizing available hardware. It means the benchmark scales across a wide variety of MCUs and SoCs.

When possible, the components are implemented in 32-bit floating point, with the exception of the neural net, which is signed 8-bit integer. The data that flows in between components is signed 16-bit integer.

Building

Typically this benchmark would be built within a product's specific environment, using their own SDK, BSP and methodologies. Given the diversity of build environments, EEMBC instead provides a simpler self-hosted main.c and an implementation using Arm's platform-agnostic CMSIS functions to quickly examine the functionality of the benchmark on an OS that supports cmake. Ideally the target platform would use its own DSP and neural-net acceleration APIs.

Linux and macOS

To build the self-hosted example, from the root directory type":

% mkdir build && cd build
% cmake .. -DPORT_DIR=ports/arm
% make test
% make audiomark
% ./audiomark

This will run the benchmark for at least 10 seconds and produce a score.

Windows

x64 Native Tools Command Prompt for VS2019 cmake for windows will create a solution (audiomark.sln) file which can be opened and compiled within Visual Studio.

Porting

AudioMark has two port layers, one for the EEMBC code, and one for the SpeeX DSP library. Both of these must be adjusted for the target platform, and both layers differ significantly. They are orthogonal, meaning the EEMBC layer is not above or below the SpeeX layer.

The SpeeX layer contains multiple levels of abstraction:

The ISA level: These are macros that perform simple math functions that could potentially be swapped for a CPU instruction, like MULT16_32_P15. These macros have generic definitions in the file fixed_generic.h.

The DSP library level: These are typically more advanced functions that can be swapped out rather than optimizing at the ISA level. For example, the FFT is abstracted out entirely. See the fftwrap.c file for multiple examples of how an FFT can be instantiated.

The application level: One step above the DSP level we have functions that aren't commonly found in libraries, such as update_gains_critical_bands. There are dozens of optimization options at this level which are described below.

The EEMBC layer only focuses on the DSP-library level, with the exception of the neural-net intitialization and inference functions. As a result, there are far fewer function calls to consider with the EEMBC layer.

Word of warning

With all of these options, it is possible to accidentally (or intentionally) create a port that runs faster at the expense of quality, thus skewing comparisons. However, for a score to be considered valid, it must pass the unit tests. There are five unit tests in total:

Digital Signal Processing (DSP) tests
- test_abf (tests/test_abf_f32.c)
- test_anr (tests/test_anr_f32.c)
- test_aec (tests/test_aec_f32.c)
- test_mfcc (tests/test_mfcc_f32.c)
Neural-network (NN) test
- test_kws (tests/test_kws.c)

The DSP unit tests permit at most 50 dB of SNR (Signal-to-Noise ratio), a failure of a unit test means the optimizations have gone too far to be considered a fair comparison. The KWS unit test use a 35dB ratio for the comparison but only when a valid data is present.

Note: The actual test codes use Noise-to-Signal ratio instead of Signal-to-Noise. SNR has the disadvantage that its value becoming infinity when the results are bit-exact match to reference data (i.e. noise level is 0).

EEMBC port layer

The EEMBC port layer is contained in two files: th_types.h and th_api.c. An unimplemented empty set of files is provided in ports/barebones, and a reference can be found in ports/arm_cmsis.

The th_types.h file defines the floating-point type, as well as 2D matrix object type, and both real and complex FFT object types.

The th_api.c file contains the definition of the following functions:

Standard library overrides

th_malloc
th_free
th_memcpy
th_memmove
th_memset

The memory allocation and free functions depend on the memory types of the platform, and how the system integrator wishes to place data in memory. See the section on the Memory Model in this document for more details. The top of the th_api.c file defines all of the static memory buffers required.

FFT functions

th_cfft_init_f32
th_cfft_f32
th_rfft_init_f32
th_rfft_f32

These functions initialize an FFT type variable and perform complex and real FFTs.

Fundamental math functions

th_absmax_f32
th_int16_to_f32
th_f32_to_int16
th_cmplx_mult_cmplx_f32
th_cmplx_conj_f32
th_cmplx_dot_prod_f32
th_cmplx_mag_f32
th_add_f32
th_subtract_f32
th_multiply_f32
th_dot_prod_f32
th_offset_f32
th_vlog_f32
th_mat_vec_mult_f32

Throughtout the EEMBC code, these functions perform the heavy-lifting.

Neural-net functions

th_nn_init
th_nn_classify

The neural-net functions are a bit different than the DSP functions. Where a math function like add is straightforward, the initialization and invocation of the inference is extremely hardware dependent.

To provide maximum flexibility, the neural net topology, layers, weights, and biases are all frozen and cannot be modified. Instead, the system integrator must construct the neural net from the definitions in src/ee_nn.h and src/ee_nn_tables.c.

LibSpeexDSP optimizations

The AEC and ANR AudioMark components which are part of the LibSpeexDSP, can be enhanced with architecture specific routines, taking advantage of the underlying CPU capabilities. There are three types of optimizations available:

FFT - FFT can already be replaced with optimized variants thanks to the existing FFT wrapper (lib/speexdsp/libspeexdsp/fftwrap.c) and some parts of the resampler already have SIMD support for ARM Neon and Intel SSE. Additional FFT operations can be added here.
Intrinsic primitives - These are simple macros that come with several variants (here is the generic implementation of a multiplication: MULT16_16 and may be replaced as needed. SpeeX comes with several compiler-time selected options stored in the files fixed_*.c.
Override path - This method uses define macros to override individual functions and is described below.

For the rest of the software, and given the monolithic nature of the LibSpeexDSP structure, customization of intensive parts could be achieved by defining compiler conditions that would override some of the inner loops with optimized routines that could potentially be vectorized or hardware-accelerated. This is similar to what was implemented for the TriMedia porting (referring the lib/speexdsp/libspeexdsp/tmv folder)

By default, none of these override compiler directives are defined, causing the LibSpeexDSP library to act with its vanilla behaviour. However, the system integrator is free to define all or part of these compiler conditional options. The decision on which of these should be set active is largely architecture and compiler dependant. Simple loop structures are typically properly handled by recent compilers which support vectorization for SIMD targets, but more complex ones could require access to an external library like CMSIS DSP, hand optimization through C with intrinsic or even assembly to reach peak performance.

As a first example, the AEC power_spectrum routine, which is essentially computing the squared magnitude of a complex signal, could use the CMSIS-DSP arm_cmplx_mag_squared_f32 function and for this defining the OVERRIDE_MDF_POWER_SPECTRUM would deactivate original definition and use the optimized variant that will be placed in the lib/speexdsp/libspeexdsp/mdf_opt_helium.c and defined the following way:

Here is the generic example in mdf_opt_generic.c:

static void power_spectrum(const spx_word16_t * X, spx_word32_t * ps, int N)
{
    int             i, j;
    ps[0] = MULT16_16(X[0], X[0]);
    for (i = 1, j = 1; i < N - 1; i += 2, j++) {
        ps[j] = MULT16_16(X[i], X[i]) + MULT16_16(X[i + 1], X[i + 1]);
    }
    ps[j] = MULT16_16(X[i], X[i]);
}

While it is possible to gain performance improvement by overriding MULT16_16, a better optimization could be attained by overriding the function. Here is an example from mdf_opt_helium.c:

void power_spectrum(const spx_word16_t * X, spx_word32_t * ps, int N)
{
    ps[0] = MULT16_16(X[0], X[0]);
    arm_cmplx_mag_squared_f32(&X[1], ps + 1, N - 1);
}

For this one, most of the recent compilers will be able to vectorize the native implementation. Visual inspection of the generated assembly would determine whether it is worth having a specific optimized variant.

A second example would be the ANR final stage overlap-add with 16-bit integer conversion. For this one, there is no native library equivalent. A customized variant with SIMD C intrinsic will allow to take advantage of SIMD if available.

As before, here is the generic implementation from preprocess_opt_generic.c:

static void vect_ola(const spx_word16_t * pSrcA, const spx_word16_t * pSrcB, spx_int16_t * pDst, uint32_t blockSize)
{
    int             i;
    
    for (i = 0; i < blockSize; i++)
        pDst[i] = WORD2INT(ADD32(EXTEND32(pSrcA[i]), EXTEND32(pSrcB[i])));
}

And here would be the ARM with Helium intrinsics version proposal found in preprocess_opt_helium.c:

void vect_ola(const spx_word16_t * pSrcA, const spx_word16_t * pSrcB, spx_int16_t * pDst, uint32_t blockSize)
{
    int16x8_t converted = vdupq_n_s16(0);
    
    for (int i = 0; i < blockSize; i += 4) {
        float32x4_t vtmp = vaddq(vld1q(pSrcA), vld1q(pSrcB));
        /* rounding, saturation and 16-bit narrowing with saturation */
        converted = vqmovnbq(vuninitializedq(converted), vcvtaq_s32_f32(vtmp));
        vstrhq_s32(pDst, converted);
        pDst += 4;         
        pSrcA += 4;         
        pSrcB += 4;
    }
}

All override compiler defines and associated C routines prototypes will be listed below. These are divided into 3 categories, associated to:

Echo canceller core (lib/speexdsp/libspeexdsp/mdf.c),
Noise suppressor core (lib/speexdsp/libspeexdsp/preprocess.c)
Associated filter banks, which are noise suppressor subparts for psychoacoustic analysis (lib/speexdsp/libspeexdsp/filterbank.c)

Code behaviour for these different C routines is respectively provided in:

lib/speexdsp/libspeexdsp/mfd_opt_generic.c
lib/speexdsp/libspeexdsp/preprocess_opt_generic.c
lib/speexdsp/libspeexdsp/filterbank_opt_generic.c

These are provided as models, replicated as-is from the core LibSpeexDSP software parts with function embedding and must serve as reference for optimized software equivalent.

It is expected to pass these compiler directives inside build systems like cmake or configuration header as with the standard LibSpeexDSP config.h.

An example of use can be found in the ARM CMSIS build YML files (platform/cmsis/speex.clayer.yml) where most of these override defines have been activated.

      misc:
        - C:
          - -DFLOATING_POINT
          - -DEXPORT=
          - -DOS_SUPPORT_CUSTOM
          # speex boosted routines
          # FilterBank
          - -DOVERRIDE_FB_COMPUTE_BANK32
          - -DOVERRIDE_FB_COMPUTE_PSD16
          # ANR
          - -DOVERRIDE_ANR_VEC_MUL
          - -DOVERRIDE_ANR_UPDATE_NOISE_ESTIMATE
          ...

Complete list of overrides, functions, and behavior.

Override	Function	Beahvior
OVERRIDE_MDF_ADJUST_PROP	mdf_adjust_prop	Computes filter adaptation rate, proportional to inverse of weight filter energy.
OVERRIDE_MDF_CONVERG_LEARN_RATE_CALC	mdf_non_adapt_learning_rate_calc	Part of the process of the computing the adaption rate when filter is not yet adapted enough. This routine divides the adaptation rate by Far End power over the whole subframe.
OVERRIDE_MDF_DC_NOTCH	filter_dc_notch16	Notch filter with strided spx_int16_t (int16_t) type input and spx_word16_t (floating point) output.
OVERRIDE_MDF_DEEMPH	mdf_deemph	Compute error signal, check input saturation and convert / saturate strided output to spx_int16_t (int16_t).
OVERRIDE_MDF_FILTERED_SPEC_AD_XCORR	void filtered_spectra_cross_corr	Compute filtered spectra and (cross-)correlations.
OVERRIDE_MDF_INNER_PROD	mdf_inner_prod	Dot-product. Was already provided as a stand-alone function in the original software.
OVERRIDE_MDF_NORM_LEARN_RATE_CALC	mdf_nominal_learning_rate_calc	Normal learning rate calculation once we're past the minimal adaptation phase.
OVERRIDE_MDF_POWER_SPECTRUM	power_spectrum	Compute power spectrum of a complex vector.
OVERRIDE_MDF_POWER_SPECTRUM_ACCUM	power_spectrum_accum	Same as power_spectrum above, with extra accumulation.
OVERRIDE_MDF_PREEMPH_FLT	mdf_preemph	Copy spx_word16_(int16_t) input data to buffer and apply pre-emphasis filter.
OVERRIDE_MDF_SMOOTHED_ADD	smoothed_add	Blend error and echo residual to apply a smooth transition to avoid introducing blocking artifacts.
OVERRIDE_MDF_SMOOTH_FE_NRG	smooth_fe_nrg	Smooth far end energy estimate over time.
OVERRIDE_MDF_SPECTRAL_MUL_ACCUM	spectral_mul_accum	Compute cross-power spectrum of a complex vectors and accumulate. Only relevant for fixed-point as mixes spx_word16_t and spx_word32_t. Floating point version, used in Audiomark, has only plain floating point versions and does not distinguish with spectral_mul_accum16.
OVERRIDE_MDF_SPECTRAL_MUL_ACCUM16	spectral_mul_accum16	Same as spectral_mul_accum above but plain spx_word16_t format. Floating point version, used in Audiomark, has only plain floating point versions and does not distinguish with spectral_mul_accum.
OVERRIDE_MDF_STRIDED_PREEMPH_FLT	mdf_preemph_with_stride_int	Strided spx_int16_t (int16_t) pre-emphasis filter with saturation check.
OVERRIDE_MDF_VEC_ADD	vect_add	spx_word16_t (Floating point) vector addition.
OVERRIDE_MDF_VEC_CLEAR	vect_clear	spx_word16_t (Floating point) vector clear.
OVERRIDE_MDF_VEC_COPY	vect_copy	spx_word16_t (Floating point) vector copy.
OVERRIDE_MDF_VEC_MULT	vect_mult	spx_word16_t (Floating point) vector multiplication for windowing.
OVERRIDE_MDF_VEC_SCALE	vect_scale	spx_word16_t (Floating point) vector scaling.
OVERRIDE_MDF_VEC_SUB	vect_sub	spx_word16_t (Floating point) vector subtraction.
OVERRIDE_MDF_VEC_SUB_INT16	vect_sub16	spx_int16_t (16-bit signed integer point) vector subtraction for filtered echo computation, difference of AEC input and output subframes.
OVERRIDE_MDF_WEIGHT_SPECT_MUL_CONJ	weighted_spectral_mul_conj	Compute weighted cross-power spectrum of a complex vector with conjugate.
OVERRIDE_ANR_APOSTERIORI_SNR	aposteriori_snr	Compute A-posteriori / A-priori SNRs.
OVERRIDE_ANR_APPLY_SPEC_GAIN	apply_spectral_gain	Apply computed spectral gain.
OVERRIDE_ANR_COMPUTE_GAIN_FLOOR	compute_gain_floor	Compute the gain floor based on different floors for the background noise and residual echo.
OVERRIDE_ANR_HYPERGEOM_GAIN	hypergeom_gain	compute hypergeometric function.
OVERRIDE_ANR_OLA	vect_ola	spx_word16_t vector overlap and add.
OVERRIDE_ANR_POWER_SPECTRUM	power_spectrum	Complex magnitude squared of a spx_word16_t (floating-point) vector.
OVERRIDE_ANR_QCURVE	qcurve	Compute 1 / (1 + 0.15 / (SNR_SCALING_1 * x))
OVERRIDE_ANR_UPDATE_GAINS_CRITICAL_BANDS	update_gains_critical_bands	Update gains in critical bands (MEL scale).
OVERRIDE_ANR_UPDATE_GAINS_LINEAR	update_gains_linear	Update gains in linear spectral bands.
OVERRIDE_ANR_UPDATE_NOISE_ESTIMATE	update_noise_estimate	Update noise estimates.
OVERRIDE_ANR_UPDATE_NOISE_PROB	update_noise_prob	Update noise probabilities and smoothed power spectrum.
OVERRIDE_ANR_UPDATE_ZETA	preprocess_update_zeta	Update Smoothed a priori SNR.
OVERRIDE_ANR_VEC_CONV_FROM_INT16	vect_conv_from_int16	Convert spx_int16_t (16-bit signed integer) vector to spx_word16_t (floating-point).
OVERRIDE_ANR_VEC_COPY	vect_copy*	Generic vector copy.
OVERRIDE_ANR_VEC_MUL	vect_mult*	`spx_word16_t` vector multiplication for windowing.
OVERRIDE_FB_COMPUTE_BANK32	filterbank_compute_bank32	Convert linear power spectrum in MEL perceptual scale.
OVERRIDE_FB_COMPUTE_PSD16	filterbank_compute_psd16	Compute the linear power spectral density from MEL perceptual scale.

* It can be noted that ANR vector multiplications and vector copy routines are similar to AEC ones and can be shared.

Memory model

There are four types of memory required for the benchmark: input audio buffers, pre-defined inter-component buffers, constant tables, and component-specific scractch memory requests.

Input audio buffers

Three channels of input audio are provided: left, right, and noise. There are roughly 1.69 seconds of audio in these buffers. A fourth buffer, for_asr is used for propagate the AEC output.

Inter-component buffers

Each component connects to the other components or inputs via one or more buffers. These statically allocated buffers' storage is defined in th_api.c to allow the system integrator to better control placement.

Table constants

All files with the name *_tables.c define arrays that are referenced via extern from their respective components. These array variables have been stored in their own source files to facilitate linker placement optimization.

The adaptive beamformer, MFCC feature extractor, and neural net all have several large tables of constants.

Dynamic allocation for scratch memory

Each component needs a certain amount of scratch memory. The components are written in such a way that they are first queried to indicate how much memory they require, and then that is allocated by the framework and provided via buffers during reset and run. This has been abstracted down to the th_malloc function. The parameters to this function are the number of required bytes and the component that is requesting it. The system integrator can use the default STDLIB malloc function, or allocate their own memory buffers to target different types of memory. The memory is never freed so there is no need to install a sophisticated memory manager. Simply assigning subsequent address pointers is sufficient (provided there is enough memory).

Both the LibSpeexDSP and EEMBC-provided components utilize this dynamic allocation pattern.

Coding conventions

Formatting

EEMBC formats according to Barr-C Embedded Standards. The .clang-format file in the root directory observes this. This file can be used within VSCode or MSVC, however it isn't clear if this behaves the same as clang-format version 14 (which aligns pointer stars differently).

Functions and filenames

Traditionally, all functions and files start with either ee_ or th_, where the former notation indicates "thou shall not change" and the latter must be changed in order to port the code (i.e., the Test Harness, hence th_).

Since there is so much code from Xiph that we are including, we will not change all of their code but might have to change some (like FFT wrappers). It may require a Run Rule to avoid this code being altered. Ideally every function should fall into a simple th_api folder or collection of files so that it is obvious what needs to be ported. Currently the components/eembc folder illustrates this by separating all of the Arm-specific code into th_api.c.

Run rules

The minimum resolution of the system timer used for performance measurement shall be one (1) kHz.
The minimum runtime shall be 10 seconds iterating on the audiomark_run() function. At least 10 iterations must complete in this 10 seconds, otherwise the runtime shall be whatever achieves 10 iterations. 10 seconds of runtime with a 1 ms resolution is effectively 0.01% measurement resolution.
Only functions and files starting with th_* may be altered. In the case of the th_api, this is required. The one exception to this is the SpeeX DSP library code, where the user may modify the wrapfft.c to install the optimal FFT, change the override macros and provide their own associated functionality, or modify the primitive DSP math macros. All of the EEMBC provided ee_ functions ultimately perform DSP computation through a subset of functions declared in the ee_api.h header, and implemented however the system integrator choses (the example puts all of the implementation reference code in the th_api.c file).
For a score to be considered valid, it must pass the AEC, ANR, ABF, MFCC and KWS regression tests found in the tests/ directory. The AEC, ANR, MFCC and ABF tests utilize a SNR ratio check of -50 dB over 62.5 ms frames of data. The KWS expects the softmax output to match the Top-1 prediction for each inference, and not the actual probability. This allows for flexibility in optimizing the API functions which may not be bit-exact, but still achieve roughly the same fidelity.
All processing must be carried out on the platform locally and not sent to the cloud. The definition of a device includes single chip silicon, and multi-die modules, and multi-chip platforms. External memories are allowed.
The AudioMark score shall be computed as "iterations per second * 1000 / 1.5". AudioMark/MHz is simply AudioMark divided by the highest core frequency in MHz. For example, if the benchmark is running on Core A at 100 Mhz and Core A uses a DSP peripheral running at 300 MHz, the highest frequency is used in the computation; in this case, 300 MHz. See footnote #1 below.
The benchmark score shall be obtained with the serial computation of the following components--ABF, AEC, ANR, KWS--with execution proceeding from one component to another, on completion. The benchmark shall not be altered or implemented in any way that causes any of the components to execute in parallel.
Running multiple processes of AudioMark on a platform is allowed. For example, a dual core product could run one instance of AudioMark (time_audiomark_run()) on each core For N cores running the AudioMark concurrently:

    Define: AM(n) is defined in Rule #6. measured per core n
    Define: f(n) is the clock frequency of core n
    AudioMark (AM) = Sum {AM(n): n = 1 to N}
    AudioMark/MHz (AM/MHz) = AM divided by max {f(n): n = 1 to N}

Footnote #1: Explanation of scoring equation

First, the 1000 factor is introduced to scale the score into a preferred integer range, this is a common EEMBC technique to avoid comparing small fractional numbers. Second, a scaling ratio is added, which was originally intended to compensate for the ratio between the sampling rate and the number of samples being processed in each iteration. The benchmark assumes a pipeline operating on 16 kHz audio input, and the idea is that a platform that is operating efficiently would score 1.0 (with no scalar): it is running exactly at the speed needed, no faster, no slower. A platform with half the performance would measure 0.5 iterations per second. However, the benchmark input dataset is actually 24000 samples worth of data not 16000, so if one iteration completes in one second, the benchmark has in reality performed better than the 16 kHz design goal. To adjust for this, the score should have been multiplied by 1.5. (Note: The benchmark could reduce the number of samples to one second (or 16k samples), however, the lead-in silence is needed to stabilize the ANR, and the keyword utterance spills over the one-second mark of the sample.). However, due to an error a divisor of 1.5 was used in the released code. To avoid confusion for people that are already using AudioMark in their projects, we have decided to keep the current code.

Submitting scores

A score must be submitted to the website before it can be used in any external publication such as: academic journals, technical papers, and marketing assets. An unsubmitted score may only be discussed internally or with a 3rd party under NDA (Non-Disclosure Agreement), but not presented to the public in any way. Note: the score may be submitted but not go live (i.e., visible on the web) until a certain date agreed to between the submitter and EEMBC. This is to account for product launch schedules.
A submitted score shall be from hardware that is available for purchase to any member of the general public. Scores for hardware that is yet to be announced must be marked "preliminary". This score may be superseded by a new score on the released product, or cleared by request to EEMBC after the product is launched.
A score collected from simulation cannot be submitted. The measured score must come from actual silicon. This includes CPU, MCU, MPU, SoC, and FPGA prototypes.

Revision history

v1.0.0 First release (4 Feb 2023)
v1.0.1 (6 Feb 2023)
- Fixes a potential bug where a multiply may goes out of bound
v1.0.2 (6 Sept 2023)
- Fix an issue in the KWS NN model. The reference C model was converted from the KWS NN TensorFlow Lite model, and there is a small lost of accuracy in the conversion process. After the code update, the reference C model fully match the original model.
v1.0.3 (14 June 2024)
- Fix incorrect use of restrict keyword #61
- KWS unit test switched from bit exact checking to Noise to Signal ratio checking.
- Most of the double precision floating-point operations in SpeexLib library code replaced by single precision.
- Changes in unit test to clarify the use of Noise-to-Signal ratio instead of Signal-to-Noise (SNR) ratio
- Documentation update: Score calculation formula uses 1/1.5 scaling rather than 1*1.5.
- Documentation update: Clarify that only float version of SpeexDSP is supported.
- Documentation update: KWS unit test description (changed to use Noise-to-Signal ratio)
- Other minor documentation improvements.

Credits

This benchmark would not have been possible without the commitment and contributions of the working group members, and the assistance from various domain experts, including (sorted by given name):

Ashutosh Pandey, Infineon (Technical Lead)
Dmitry Utyansky, Synopsys
Fabien Klein, Arm
Felix Johnny Thomasmathibalan, Arm
Gary Jacobson: Renesas
Jim Ryan, onsemi
Joseph Yiu, Arm
Kaiping Lee, Infineon
Laurent Le Faucheur, Arm
Mark Wallis, STMicroelectronics
Nagendra (GD) Gulur Dwarakanath, Texas Instruments
Peter Torelli, EEMBC
Rita Chattopadhyay, Intel
Ruud Derwig, Synopsys

eembc / audiomark