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make me a technical analysis report from the results of the training data set -- 2

please check the file that I uploaded, understand it carefully. if you understand everything, please chat me

I need someone who can write and understand technically

- Huffman algorithm

- binary data

- music pattern

- technical writer

I've made 6 compression versions of huffman

Make me Analysis, Discussion, Explanation of the results of compression of the following 6 variants of the Huffman algorithm.

- Static / binner

- dynamic

- Quad

- Okta

- Hexa

- 4-ary / modif Quad).

The following are various types of data sets that I use (I attach a sample CSV sample and resume)

1. Music – Speech – Noise ( mono, 16 kHz, 16 Bit)

Link : [login to view URL]

MUSAN: A Music, Speech, and Noise Corpus (David Snyder and Guoguo Chen and Daniel Povey) 2015 [1]

- Music (fma = 128 files),( fma-western-art = 93 files), (hd-classical = 75files), (jamendo = 217 files), (rfm = 148 files). Total = 661 files.

- Speech (librivox = 173 files), (us-gov = 253 files). Total = 426 files

- Noise (free-sound = 843 files), (sound-b = 86 files) = 929 files

2. Music Orchset mono and stereo (44.1 kHz, 16 bit)

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J. Bosch, R Marxer, and E. Gomez. Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music. Journal of New Music Research 2016 [2]

- Stereo (64 files), Mono (64 files). Total = 128 files

3. Instrument ( stereo 44.1 kHz, 16 bit)

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IRMAS

Bosch, J. J., Janer, J., Fuhrmann, F., & Herrera, P. “A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals”, in Proc. ISMIR (pp. 559-564), 2012 [3]

- Cello (388 files), clarinet (505 files) , flute (451 files), acoustic guitar (637 files), electric guitar (760 files), organ (682 files), piano (721 files), saxophone (626 files), trumpet (577 files), violin (580 files), and human singing voice (778 files). Total 6705.

- Music (mono 22.05 KHz, 16 bit) 64 Files

- Speech (mono 22.05 KHz, 16 bit) 64 Files

4. Noise ( mono, 44.1 KHz, 16bit)

Link : [login to view URL]

FSDnoisy18k

Eduardo Fonseca, Manoj Plakal, Daniel P. W. Ellis, Frederic Font, Xavier Favory, and Xavier Serra, “Learning Sound Event Classifiers from Web Audio with Noisy Labels”, arXiv preprint [4].

arXiv:1901.01189, 2019

- FSDnoisy18k ( 947 files)

5. RipCD (stereo 44.1kHz 16 bit)

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Music Track With CD quality

Robin Whittle. Lossless audio compression, 2000-2005.[5]

- This corpus gives a good range of material, covering a variety of types of musical performances (13 Files)

Kemahiran: Algoritma, Penulisan Teknikal, Analisis Binari, Signal Processing, Penulisan Penyelidikan

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Tentang Majikan:
( 0 ulasan ) Sleman, Indonesia

ID Projek: #21669073

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