Towards a Real-Time Implementation of Loudness Enhancement Algorithms on a Motorola DSP 56600
Most of the cellular phone companies with audio speaker capabilities focus on reducing the current drain to extend battery life. None of these companies concentrate on modifying the speech signal itself to make it sound louder in noisy listener environments without adding additional energy. Such algorithms have been described in literature by Boillot and form the backbone of this thesis. The current project focusses on taking a step towards running these algorithms in real-time on a 16-bit fixed point Motorola DSP 56600. Implementation of the autocorrelation, Levinson- Durbin, FIR, and IIR filters in assembly for the Motorola DSP 56600 has been investigated in the thesis. The challenges and alternate solutions to circumvent the challenges have been described, and experimental results have been presented. Results indicate that the modified signed LMS algorithm, which can be considered to be a blend between the LMS and signed LMS algorithms, turns out to be an elegant solution to circumvent the challenges in implementing the Levinson-Durbin recursion.
Summary
This master's thesis documents steps toward running Boillot-style loudness enhancement algorithms in real time on a 16-bit fixed-point Motorola DSP 56600. It explains implementations of autocorrelation, Levinson–Durbin LPC, FIR and IIR filters in assembly, describes numerical and performance challenges on constrained hardware, and reports experimental results and workarounds toward a deployable mobile solution.
Key Takeaways
- Implement fixed-point autocorrelation and Levinson–Durbin LPC on a 16-bit DSP for speech processing.
- Optimize FIR and IIR filter implementations in Motorola DSP 56600 assembly to meet real-time constraints.
- Mitigate numerical range, stability, and quantization issues inherent to fixed-point loudness enhancement.
- Evaluate loudness enhancement trade-offs for mobile speakers without increasing transmitted energy.
- Apply assembly-level optimizations and alternative algorithms to work around performance bottlenecks.
Who Should Read This
DSP engineers, embedded systems developers, and graduate students experienced with fixed-point processors who want practical guidance on implementing real-time audio/speech enhancement on resource-constrained mobile hardware.
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