Trirat, Akraphon

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Design of multiplierless elliptic narrowband IIR digital filter based on sensitivity analysis
(2004-12-01)
Trirat, Akraphon
;
Chivapreecha, Sorawat
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Khunaworawet, Tanawut
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Ruangrangsan, Teerasak
;
Dejhan, Kobchai
In this paper, we present a technique for the design of narrow-band multiplierless IIR filter based on sensitivity analysis. All the multiplication constants in proposed filter structure are implemented with a small number of shift registers and adders. It is shown in the paper that by appropriated design of the elliptic minimal Q - factor (EMQF) prototyped transfer function, (n+1)/2 multiplication constants of highest sensitivity can be implemented without quantization. The quantization of the remaining (n-1)/2 less-sensitivity constants is performed using phase-tolerence scheme.
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Tunable bandpass/bandstop digital filters basedon 1st-order allpass network instead of unit delay
(2019-07-01)
Sutthikarn, Pemmavit
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Chivapreecha, Sorawat
;
Jongsataporn, Thitaphan
;
Trirat, Akraphon
This paper presents a design and realization of tunable bandpass and bandstop digital filters based on using tunable digital lowpass and highpass filters. The frequency response of both digital lowpass and highpass filters which called prototype filters can be changed/tuned by replacing unit delay elements in filter structure with 1<sup>st</sup>-order allpass network. From the tunable digital lowpass and highpass filters, the cascade connection of them can give tunable bandpass filter, the parallel connection can give tunable bandstop filter. The conditions of using proposed tunable bandpass/bandstop filters will be analyzed and explained in this work. A high flexibility of tuning both center frequency and bandwidth can be achieved from the proposed design and realization. Simulation results also be compared with experimental results from hardware implementation on FPGA using NI-myRIO device to ensure that the obtained frequency responses are correspondence.
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A Simple Tunable Biquadratic Digital Bandpass Filter Design for Spectrum Sensing in Cognitive Radio
(2020-06-01)
Sutthikarn, Pemmavit
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Chivapreecha, Sorawat
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Trirat, Akraphon
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Jongsataporn, Thitaphan
This article presents a design and realization of tunable digital bandpass filter based on using biquadratic filter for spectrum sensing in cognitive radio. The biquadratic bandpass digital filter structure has shown and after that the tunable approach will be derived. The bandwidth and center frequency of the proposed tunable biquadratic digital bandpass filter can be tuned by tuning parameters. In each band, energy detection by spectrum sensing method will be used by cognitive radio users (CRU) for recognize the frequency spectrum of primary user (PU). If there are not having any PU in that band, the status of band will set to spectrum hole. Eventually, CRU can utilize this channel.
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A tunable multiple outputs FIR filter structure realization
(2019-01-01)
Sutthikarn, Pemmavit
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Chivapreecha, Sorawat
;
Trirat, Akraphon
This paper proposes a design of a new finite impulse response (FIR) digital filter structure which gives 6-tunable frequency responses as 2 low-pass filters (LPF), 2 high-pass filters (HPF), 1 band-pass filter (BPF) and 1 band-stop filter (BSF) at the same time. The design procedure is initiated from 2 LPFs design. However in order to obtain a tunable filter, the unit delay is replaced by first-order single-multiplier structure for all-pass filter. Therefore, this new structure can be achieved and called tunable multiple outputs FIR filter. Finally, the amplitude responses that obtained from proposed structure can show the tunable capability using tuning parameter.
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Musical Key Classification Using Convolutional Neural Network Based on Extended Constant-Q Chromagram
(2022-01-01)
Chivapreecha, Sorawat
;
Sinjanakhom, Tantep
;
Trirat, Akraphon
In the field of music information retrieval, musical key classification is one of the challenges. This paper illustrates the advantages of the proposed system with relevant experimental results, starting with diverse audio datasets for feature extraction used for training and testing a classification model which is based on a convolutional neural network (CNN). The goal is to develop a feature that can improve the neural network's performance. To compare the effect of input features on efficiency, a basic CNN is trained from the ground up and utilized as an image classification tool. The Chromagram-24, an augmented version of the input chroma feature, is proposed to improve the accuracy of musical key detection. In terms of weighted score, the model using Chromagram-24 as an input feature outperforms the model trained using a conventional 12-dimensional chromagram by 12.77% and achieves the highest score of 85.63% when classifying full-length songs. Chromagrams are generated using audio excerpts ranging in length from 15 to 60 seconds for local key estimation, whereas, for global key estimation, a full-length audio set is used. The results indicate that, given the different lengths of training audio input, executing the model using a chromagram of a 60-second audio excerpt yields the best results.

Trirat, Akraphon

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