posted Mar 2, 2021, 8:41 PM by MUHAMMAD MUN`IM AHMAD ZABIDI
Detect dogs and cats sounds in your neighborhood!
How screen scraping and TinyML can turn any dial into an API
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posted Feb 28, 2021, 6:19 AM by MUHAMMAD MUN`IM AHMAD ZABIDI
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updated Feb 28, 2021, 6:25 AM
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posted Feb 26, 2021, 2:30 AM by MUHAMMAD MUN`IM AHMAD ZABIDI
Compeitions: Python Notebooks on Spectrograms Not on Kaggle, but related: Haytham Fayek offered this advice:
tl;dr:
Use Mel-scaled filter banks if the machine learning algorithm is not susceptible to highly correlated input.
Use MFCCs if the machine learning algorithm is susceptible to correlated input.
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posted Jan 23, 2021, 10:47 PM by MUHAMMAD MUN`IM AHMAD ZABIDI
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updated Jan 23, 2021, 11:08 PM
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- [britanak2001efficient] Vladimir Britanak and K. R. Rao, An Efficient Implementation of the Forward and
Inverse MDCT in MPEG Audio Coding, IEEE Sig Proc Letters, 2001.
- [britanak2011survey] Vladimir Britanak, A survey of efficient MDCT implementations in MP3 audio coding standard: Retrospective and state-of-the-art, Signal Processing, 2011,
- [raissi2002theory] Rassol Raissi, The Theory Behind Mp3, , December 2002.
- [jacaba2001audio] Audio Compression Using Modified
Discrete Cosine Transform: The Mp3
Coding Standard, 2001.
- [da2008mdct] Xingdong Da, An MDCT Hardware Accelerator for MP3 Audio, 2008
- [lee] Wen-Cheh Lee, Design of the Audio Coding Standards for MPEG and AC-3.
- [kok1997fast] C.W. Kok, Fast Algorithm for Computing Discrete Cosine Transform, IEEE Trans Sig Proc, 1997:
- [cheng2003fast] Mu-Huo Cheng and Yu-Hsin Hsu, Fast IMDCT and MDCT Algorithms—
A Matrix Approach, IEEE Trans Sig Proc, 2003:
- [lee2001improved] Improved Algorithm for Efficient Computation of the
Forward and Backward MDCT in MPEG Audio Coder, Szu-Wei Lee, IEEE Trans Cir Sys, 2001
- Bibliography at MP3-Tech
- Bibliography at CCRMA Stanford
- [levine1998audio] Scott N. Levine, Audio Representations For Data Compression And
Compressed Domain Processin, PhD Thesis, Stanford U, 1998.
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posted Nov 27, 2020, 11:32 PM by MUHAMMAD MUN`IM AHMAD ZABIDI
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updated Apr 13, 2021, 4:44 PM
]
- Meyer, Matthias, Lukas Cavigelli, and Lothar Thiele. "Efficient convolutional neural network for audio event detection." arXiv preprint arXiv:1709.09888 (2017). URL l{https://arxiv.org/pdf/1709.09888.pdf}
- Abdrakhmanov, Vali Kh, Renat B. Salikhov, and Konstantin V. Vazhdacv. "Development of a sound recognition system using STM32 microcontrollers for monitoring the state of biological objects." 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2018. URL l{https://ieeexplore.ieee.org/document/8545278} <-- privileged
- Crocioni, Giulia, et al. "Li-Ion Batteries Parameter Estimation With Tiny Neural Networks Embedded on Intelligent IoT Microcontrollers." IEEE Access 8 (2020): 122135-122146. URL l{https://ieeexplore.ieee.org/document/9133084}
- Li, Hao, et al. "Design of micro-automatic weather station for modern power grid based on STM32." The Journal of Engineering 2017.13 (2017): 1629-1634. URL {https://ieeexplore.ieee.org/iel7/7864294/8311003/08311183.pdf}
- Luna-Perejón, Francisco, et al. "Low-Power Embedded System for Gait Classification Using Neural Networks." Journal of Low Power Electronics and Applications 10.2 (2020): 14. URL {https://www.mdpi.com/2079-9268/10/2/14/pdf}
- Sakr, Fouad, et al. "Machine Learning on Mainstream Microcontrollers." Sensors 20.9 (2020): 2638. \url{https://www.mdpi.com/1424-8220/20/9/2638/pdf}
- Chaber, Patryk, and Maciej Ławryńczuk. "Fast analytical model predictive controllers and their implementation for STM32 ARM microcontroller." IEEE Transactions on Industrial Informatics 15.8 (2019): 4580-4590. URL l{https://www.researchgate.net/profile/Maciej_awrynczuk/publication/330372035_Fast_Analytical_Model_Predictive_Controllers_and_Their_Implementation_for_STM32_ARM_Microcontroller/links/5daf1a21299bf111d4bfbd15/Fast-Analytical-Model-Predictive-Controllers-and-Their-Implementation-for-STM32-ARM-Microcontroller.pdf}
- Lin, Yi-Bing, et al. "Morsocket: an expandable iot-based smart socket system." IEEE Access 6 (2018): 53123-53132. URL {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8466570}
- Ji, Li, Shao Qiongling, and Wang Shengjun. "Design of Embedded Network Voice Communication Terminal Based on STM32 and μCOSIII." MATEC Web of Conferences. Vol. 173. EDP Sciences, 2018. URL {https://www.matec-conferences.org/articles/matecconf/pdf/2018/32/matecconf_smima2018_03027.pdf}
- Canta, Riccardo. "Design of an optimized audio framework for portable digital MEMS microphones evaluation." (2012). Thesis. Politecnico di Milano. URL {https://www.politesi.polimi.it/bitstream/10589/72062/1/Canta_Riccardo_754573.pdf}
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posted Nov 15, 2020, 2:09 AM by MUHAMMAD MUN`IM AHMAD ZABIDI
Fig. 2 from R. Boutaba et al., “A comprehensive survey on machine
learning for networking: evolution, applications and research
opportunities,” J. Internet Serv. Appl., vol. 9, no. 1, p. 99, 2018.
Problem categories that benefit from machine learning.
a Clustering.
b Classification.
c Regression.
d Rule extraction
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posted Nov 15, 2020, 2:06 AM by MUHAMMAD MUN`IM AHMAD ZABIDI
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updated Nov 15, 2020, 2:06 AM
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Fig. 3 from R. Boutaba et al., “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities,” J. Internet Serv. Appl., vol. 9, no. 1, p. 99, 2018.
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posted Nov 15, 2020, 1:59 AM by MUHAMMAD MUN`IM AHMAD ZABIDI
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updated Nov 15, 2020, 2:03 AM
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Fig. 5 from R. Boutaba et al., “A comprehensive survey on machine learning for networking: evolution, applications and research opportunities,” J. Internet Serv. Appl., vol. 9, no. 1, p. 99, 2018.
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posted Nov 15, 2020, 1:56 AM by MUHAMMAD MUN`IM AHMAD ZABIDI
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updated Nov 15, 2020, 1:57 AM
]
Table 3 from M. G. S. Murshed, C. Murphy, D. Hou, N. Khan, G. Ananthanarayanan, and F. Hussain, “Machine Learning at the Network Edge: A Survey,” arXiv Prepr. arXiv1908.00080, pp. 1–28, 2019.
Device
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GPU
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CPU
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RAM
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Flash
memory
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Power
consumption
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Example
applications
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Raspberry Pi
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400MHz
VideoCore IV
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Quad
Cortex A53 @ 1.2GHz
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1 GB SDRAM
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32 GB
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2.5 Amp
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video analysis [78, 114]
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Coral Dev Board (Edge TPU)
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GC7000 Lite
Graphics + Edge TPU coprocessor
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Quad Cortex-A53, Cortex-M4F
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1 GB LPDDR4
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8 GB LPDDR4
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5V DC
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image processing [18]
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SparkFun Edge
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-
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32-bit ARM
Cortex-M4F 48MHz
(with 96MHz
burst mode) processor
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384KB
|
1MB
|
6uA/MHz
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speech recognition [31]
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Jetson TX1
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Nvidia
Maxwell 256 CUDA
cores
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Quad ARM A57/2 MB L2
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4 GB
64 bit LPDDR4
25.6 GB/s
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16 GB eMMC, SDIO, SATA
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10-W
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video, image analysis [68] [61]
robotics [33]
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Jetson TX2
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Nvidia Pascal
256 CUDA
cores
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HMP Dual
Denver 2/2 MB L2 +
Quad ARM
A57/2 MB L2
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8 GB
128 bit LPDDR4
59.7 GB/s
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32 GB eMMC, SDIO, SATA
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7.5-W
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video, image analysis [68], [92]
robotics [24]
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Intel Movidius Neural Compute Stick
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High Performance VPU
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Myriad 2 Vision Processing Unit
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1 GB
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4 GB
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2 trillion
16-bit operations per second within
500 mW
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classification [73] computer
vision [14, 43]
|
ARM ML
|
-
|
ARM ML
processor
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1 GB
|
-
|
4 TOPs/W
(Tera Operations)
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image, voice recognition [107]
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RISC-V GAP8
|
-
|
nona-core
32-bit RISC-V
microprocessor
@250 MHz
|
16 MiB SDRAM
|
-
|
1 GOPs/mW
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image, audio processing [35]
|
OpenMV
Cam
|
-
|
ARM 32-bit
Cortex-M7
|
512KB
|
2 MB
|
200mA
@ 3.3V
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image
processing [6]
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BeagleBone AI
|
-
|
Cortex-A15
Sitara AM5729 SoC with 4 EVEs
|
1 GB
|
16 GB
|
-
|
computer vision [25]
|
EMC3531
|
-
|
ARM Cortex-M3
NXP Coolflux DSP
|
-
|
-
|
-
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audio, video analysis38
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posted Nov 15, 2020, 1:53 AM by MUHAMMAD MUN`IM AHMAD ZABIDI
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updated Nov 15, 2020, 1:55 AM
]
Table 3 from M. G. S. Murshed, C. Murphy, D. Hou, N. Khan, G. Ananthanarayanan, and F. Hussain, “Machine Learning at the Network Edge: A Survey,” arXiv Prepr. arXiv1908.00080, pp. 1–28, 2019.
Framework | Core language | Interface | Part running on the edge | Example applications | TensorFlow Lite (Google) | C++ Java | C/C++ Java | TensorFlow Lite NN API | computer vision [109], speech recognition [42, 1] | Caffe2 Caffe2Go (Facebook) | C++ | Android iOs | NNPack | image analysis, video analysis [53] | Apache MXNet | C++ Python R | Linux MacOS Windows | Full Model | object detection, recognition [78] | Core ML2 (Apple) | Python | iOS | CoreML | image analysis [16] NLP [105] | ML Kit (Google) | C++ Java | Android iOs | Full Model | image recognition, text recognition, bar-code scaning [26] | AI2GO | C, Python Java, Swift | Linux macOs | Full Model | object detection, classification [5] | DeepThings | C/C++ | Linux | Full Model | object detection [119] | DeepIoT | Python | Ubilinux | Full Model | human activity recognition, user identification [116] | DeepCham | C++ Java | Linux Android | Full Model | object recognition [62] | SparseSep | - | Linux Android | Full Model | mobile object recognition, audio classification [15] | Edgent | - | Ubuntu | Major part of the DNN | image recognition [63] |
17 |
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