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Audio Compression using a Modified Vector Quantization algorithm for Mastering Applications

Prince Shajin, D Bini, Kirubaraj Alfred A., Immanuel Samson J., M Surya

International Journal of Electronics and Telecommunications

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2023

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Vol. 69, No. 2

287--292

EN

Audio data compression is used to reduce the transmission bandwidth and storage requirements of audio data. It is the second stage in the audio mastering process with audio equalization being the first stage. Compression algorithms such as BSAC, MP3 and AAC are used as standards in this paper. The challenge faced in audio compression is compressing the signal at low bit rates. The previous algorithms which work well at low bit rates cannot be dominant at higher bit rates and vice-versa. This paper proposes an altered form of vector quantization algorithm which produces a scalable bit stream which has a number of fine layers of audio fidelity. This modified form of the vector quantization algorithm is used to generate a perceptually audio coder which is scalable and uses the quantization and encoding stages which are responsible for the psychoacoustic and arithmetical terminations that are actually detached as practically all the data detached during the prediction phases at the encoder side is supplemented towards the audio signal at decoder stage. Therefore, clearly the quantization phase which is modified to produce a bit stream which is scalable. This modified algorithm works well at both lower and higher bit rates. Subjective evaluations were done by audio professionals using the MUSHRA test and the mean normalized scores at various bit rates was noted and compared with the previous algorithms.

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Intelligent agrobots for crop yield estimation using computer vision

Bini D., Pamela D., Mary Thusnavis Bella I., Shamia D., Prince Shajin

Computer Assisted Methods in Engineering and Science

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2022

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Vol. 29, no. 1-2 spec.

161--175

EN

The machine vision-based autonomous intelligent robots perform precise farm tasks suchas robot harvesting, weeding, pest or fertilizer spraying, monitoring, and pruning. Estimating crop yield is an essential assignment on a regional or federal scale. For a long timethe estimation measures were based on the statistics from manual counting of plants ina specific zone. The computer vision algorithms have addressed the technical drawbacksof the conventional image processing techniques and established an autonomous disciplineand yielded new approaches to crop planning. A method for quantitative assessment ofa tomato crop has been developed in this research using color thresholding in MATLAB using the RGB color model. Converting an RGB image to a grayscale image is one of thesteps involved in detecting red color in a taken image. After subtracting the two images,a median filter is employed to filter the noisy pixels to produce a two-dimensional blackand white image. The bounding boxes are used to label the binary digital images to detectrelated components, and the parameters of the labeled regions are computed to measurethe number of tomatoes in a crop. The obtained R2 correlation coefficient between thetomato berry counting algorithm and human counting was 0.98. Furthermore, the color ofeach pixel in the acquired image is evaluated by examining RGB values for pixel intensitiesin the obtained image. The performance of the berry counting algorithm was evaluated,and the technique was determined to have a high precision and recognition ratio of 96%.The research indicates that this technique may be used to estimate the crop yield, whichis helpful information for forecasting yields, planning harvest plans, and generating prescription maps for field-specific management strategies. The proposed model performedexceptionally well in estimating yield with each tomato (Solanum lycopersicum) crop.

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Analysis of FPGA accelerator architecture for Fast Statistical Convolutional Neural Network in real time Emotional Recognition System

Immanuel Samson J., Manoj G., Prince Shajin, Du John Victor H.

Przegląd Elektrotechniczny

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2021

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R. 97, nr 7

132--134

EN

Deep learning, an artificial intelligence area that emerged as a consequence of later developments in computerized innovation and the accessibility of data knowledge, has demonstrated its skill and adequacy in coping with complex learning problems that were previously unthinkable. (CNNs). Convolution neural network has shown the feasibility of emotional detection and acknowledging unique applications. In any case, concentrated processor activities and memory transfer speed are required, which causes general CPUs to fall short of achieving optimal execution levels. Following that, equipment quickening agents using General Processing Units (GPUs), Field Programmable Gate Array (FPGAs), and Application Specific Integrated Circuits (ASICs) were used to increase the throughput of CNNs. In addition, we include rules for improving the use of FPGAs for CNN speedup. The proposed algorithm is implemented on an FPGA platform, and results show that emotions regonition utterances of 1.25s are found in 1.85ms, consuming 85% of the resources. This illustrates the suitability of our approach for real-time Emotional Recognition device applications.

PL

Deep learning, dziedzina sztucznej inteligencji, która pojawiła się w wyniku późniejszych postępów w skomputeryzowanych innowacjach i dostępności wiedzy na temat danych, dowiodła swoich umiejętności i adekwatności w radzeniu sobie ze złożonymi problemami uczenia się, które wcześniej były nie do pomyślenia. Neuronowa sieć konwolucyjna wykazała wykonalność wykrywania emocji i rozpoznawania wyjątkowych zastosowań. W każdym razie wymagane są skoncentrowane działania procesora i szybkość transferu pamięci, co powoduje, że ogólne procesory nie osiągają optymalnych poziomów wykonania. W celu zwiększenia przepustowości CNN, zastosowano środki przyspieszające sprzętu, wykorzystujące jednostki przetwarzania ogólnego (GPU), programowalną macierz bramek (FPGA) i układy scalone specyficzne dla aplikacji (ASIC).. Proponowany algorytm jest zaimplementowany na platformie FPGA, a wyniki pokazują, że wypowiedzi regonacji emocji o długości 1,25s znajdują się w czasie 1,85 ms, co pochłania 85% zasobów. To ilustruje przydatność naszego podejścia do aplikacji urządzeń do rozpoznawania emocji w czasie rzeczywistym