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Hardware implementation of an MPEG-1 Layer-1 audio encoder

EE4 Final Year project 2000-2001. Hardware implementation of an MPEG-1 Layer-1 audio encoder. Sami Khawam. S.Khawam@ug.ee.ed.ac.uk. Friday, January 26, 2001. Presentation Outline. 1. Aims of the project 2. Overview of MPEG algorithm 3. Implementation 4. Project organisation

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Hardware implementation of an MPEG-1 Layer-1 audio encoder

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  1. EE4 Final Year project 2000-2001 Hardware implementation of an MPEG-1 Layer-1 audio encoder Sami Khawam S.Khawam@ug.ee.ed.ac.uk Friday, January 26, 2001

  2. Presentation Outline 1. Aims of the project 2. Overview of MPEG algorithm 3. Implementation 4. Project organisation 5. Further extensions and modifications

  3. 1. Aims of the project

  4. Aims of the project • Hardware core for MPEG-1, layer-1 (MP1) audio encoding. • Description in Verilog-RTL for flexibility. • Synthetisable to FPGA architecture.

  5. Advantage • High speed, thus real-time possibility. • Lower power consumption. • Low cost by using low cost FPGAs. Commercial IP cores and chips exist, but are mainly based on software and DSP cores. The advantages in hardware:

  6. Applications • Portable or standalone audio record and playback devices. • Voice-over-IP systems. • Standalone internet-radio servers.

  7. 2. Overview of MPEG algorithm

  8. Encoding System

  9. Analysis Filterbank • Time to frequency domain conversion. • Critically sampled: 384 inputs, 384 outputs. • 32 equal width subbands. • Like parallel band-pass. • Equation from ISO document: • Can be optimised

  10. Psychoacoustic Model • Key to maintain signal quality at high compression ratios. • Based on absolute hearing threshold • and the frequency masking properties of the ear. • Finds the Signal-to-Mask Ratio value for each subband.

  11. SMR calculation outline • 512-point FFT and window then squaring to get power. • Identify tonal (sine like, local peaks) and non-tonal (noise like) maskers. • Remove the ones that are blow the threshold of hearing. • Remove weak tonal maskers “too close” to strong maskers. • Find global masking threshold = absolute threshold + contribution from maskers. • Find minimum masking threshold in each subband (from individual masking threshold and GMT). • SMR of each subband = MMT - Peak power of subband.

  12. Bit allocation and Quantisation • Find the minimum number of quantisation bits to make the quantisation noise inaudible. • Uses iteration to find the minimum difference between SMR and SNR. • Attempts to get the desired final bit rate. • Then linearly quantise the subband samples.

  13. Bitstream formatting • Header • For each of the 32 subbands: • number of bit allocated • scale factor • quantised samples • Optional CRC error check.

  14. 3. Implementation • Audio encoded frame by frame • PC sends PCM audio and receives the encoded data. • Encoded data saved in .mp1 format by the PC. FPGA PC 384 PCM frame RS232 MPEG RS232 driver Encoder Encoded data

  15. 4. Project Timeline

  16. Second Term • Build the 3 main components separately then integrate them. • For each component: • Design the hardware based on C and Matlab source. • Write adequate test-benches for simulation.

  17. Third Term • Implement the integrated blocks and RS232 transfers on the FPGA. • Write C program for transferring data between the PC and the board. • Create test samples. • Write documentation.

  18. 5. Further extensions and modifications

  19. Problematic issues • FPGA too small. • Low memory availability of the FPGA; external DRAM needed. • Psychoacoustic model too design-time expensive.

  20. Possible solutions • Lower complexity psychoacoustic model. e.g. using FFT’s find the average sound power level for each subband and treat result as SMR. • Computation of standard functions on the PC, e.g. FFT in order to save memory and design time.

  21. Further extension • Support for layer 2. • Implementation of decoder. • Optimisation (speed and size) of single components, e.g. like the filterbank.

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