MelKWS Engine

A simple and resource efficient hardware accelerator designed specifically for Keyword Spotting (KWS) applications using log-mel spectrograms as the audio feature extractor.

Architecture

Description

1. Input:

   • The input audio stream is sampled at a specific frequency, such as 16 kHz.
   • Each audio frame consists of a fixed number of samples.

2. Log-Mel Spectrogram Computation:

   • Implement a lightweight log-mel spectrogram computation module to extract features from the input audio stream.

3. Keyword Detection:

   • The accelerator should detect the presence or absence of a single predefined keyword or command based on the computed log-mel spectrograms.

4. Output:

   • Provide a mechanism to indicate the presence or absence of the keyword in the input audio stream.
   • Output a binary flag signal indicating the presence or absence of the keyword.

Architecture Choice

1. Input Interface:
   • Purpose: Handles incoming audio samples, ensuring they are correctly timed and formatted for processing.
   • Components:
     • Sample buffer: Temporarily stores incoming audio samples.
     • Control logic: Manages the flow of samples based on system state and input validity.
2. Pre-processing:
   • Purpose: Applies necessary pre-processing steps to the audio samples, such as framing and windowing.
   • Components:
     • Frame buffer: Segments the continuous audio stream into overlapping frames.
     • Window function: Applies a windowing function to each frame to minimize spectral leakage.
3. FFT Module:
   • Purpose: Converts time-domain audio frames into frequency-domain representations using the Fast Fourier Transform (FFT).
   • Components:
     • FFT processor: Computes the FFT of each windowed frame.
4. Mel Filterbank Processing:
   • Purpose: Applies a set of Mel-scaled filters to the FFT output to extract frequency bands that mimic human auditory perception.
   • Components:
     • Filterbank: A collection of band-pass filters corresponding to the Mel scale.
     • Energy computation: Calculates the energy in each Mel band.
5. Feature Extraction:
   • Purpose: Optionally extracts additional features from the Mel spectrogram, such as MFCCs (Mel Frequency Cepstral Coefficients), if required by the keyword detection logic.
   • Components:
     • Feature extractor: Calculates MFCCs or other features from the Mel spectrogram.
6. Dynamic Precision Adjustment:
   • Purpose: Adjusts the precision of the FFT or Mel spectrogram data to optimize for computational efficiency or resource usage.
   • Components:
     • Precision control: Dynamically adjusts data bit-width based on configurable criteria.
7. Logarithmic Compression:
   • Purpose: Applies logarithmic compression to the Mel spectrogram to better match the non-linear perception of loudness in the human auditory system.
   • Components:
     • Logarithmic function: Computes the logarithm of Mel spectrogram values.
8. Keyword Detection Logic:
   • Purpose: Analyzes the log-Mel spectrogram (and possibly additional features) to detect the presence of specific keywords.
   • Components:
     • Detection algorithm: Implements a simple thresholding or a more complex pattern matching/machine learning algorithm to identify keywords.
     • Keyword selector: Allows dynamic selection of the keyword(s) to be detected.
9. Output Interface:
   • Purpose: Indicates the detection result, such as the presence of a keyword.
   • Components:
     • Detection output: Signals when a keyword has been detected.
     • Status indicators: Provide additional information about the detection process, such as confidence levels.
10. Integration and Control (Top):
    • System Controller: Coordinates the operation of all stages, managing state transitions, processing flow, and synchronization.
    • Clock and Reset Management: Ensures all components operate synchronously and can be reset to a known state.

Forked from the Caravel User Project

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