STRUCTURE

STRUCTURE

1. TITLE

2. INTRODUCTION

   • SYNTHESIS EVALUATION (TERMS)- in historical perspective list types and how their char. affect prevalence. Where GS sits in this categorization. ie score it's strengths/weaknesses - control heterogenous, like FM, FM managed to break into commercial market place. FM however, is a highly efficient algorithm. Therefore, issue to be inves. algorihtmic efficiency.
   • MICROCONTROLLERS GS control does not require same control structure as trad. synthesis and is well suited to teh physical computing env poss with MPUs using natural gestures to control each control channel. Is is therefo poss to use MPUs as the control interface, using DSP on Pcs to generate the sound. - so if the GS engine can be built wihtin the MPU, a standalone instr. suited to the performance req of GS might see the particle based synthesis as a viable popular alternative to wavebased synthesis methods
   • INTRODUCE GS The focus of this investigation is GS. Introduce GS, it's characteristics. It has implementations in graphical and text based high level applications. However, as a synthesis method it doesnt have a sig. place commercially. In order to understand this, it needs to be evaluated in the context of digital synthesis.

3. BACKGROUND

   • GS IMPLEMENTATIONS look at GS 1 prePC 2 in 1990s 3 current. study methods that can be characterised as most algorithmically efficient in terms of processing requirements (power and speed) memory (prog and data) and control stream rates and density

4. METHODOLOGY

   • INTRODUCTION
   • AIMS/OBJECTIVES
     • PERCEPTUAL / QUALITATIVE
       • TEST-SET3
         • SUBJECTIVE LISTENING TESTS
           • TEST DESIGN
           • METRIC ANOVA
     • QUANTITATIVE/PHYSICAL TESTS
       • TEST-SET1 GS ALGORITHMS
         • HOW TESTS WILL BE CONDUCTED
           • TOOLS
           • METRIC
       • TEST-SET2 MPU BENCH EVALUATIONS
         • HOW TESTS WILL BE CONDUCTED
           • TOOLS
           • METRIC

5. RESULTS

   • TABULATE RESULTS

6. PROPOSAL

   • From this discersive research, grain-generator, sound object and global parameters will be tested for AE in terms of: nos of Unit Generators, nos of parallel audio rate and control rate streams. Granulation is familiar and successful commercially as an effects processor, a time domain mechanical process, using DSP processors to granulate sound files. However, the aim of this study is to find out if G synthesis is possible with the constraints of microcontroller processors and peripherals in the environment designed for and popular with artists/msucians, Arduino. Therefore, the limitations and MPU architecture need to be considered in the design of the GS algorithms to be tested. These algorithms will be tested and ranked. These results will be compared with microcontroller performance capabilities and the ability for the algorithm to be implemented be assessed.

7. CONCLUSION

   • FUTURE RESEARCH
   • Answer question - with Perceptual Transparency qualification
     • WHAT QUES DOES IT POSE
       • OTHER MICROCONTROLLERS ?
       • DSP PROCESSORS E.G. PICDSP

8. BIBLIOGRAPHY

9. APPENDICES

   • SPREADSHEETS
     • COMPARISON/ANALYSIS