• Proceedings of the KOSOMBE Conference
• /
• v.1992 no.05
• /
• pp.175-178
• /
• 1992

This paper proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex. the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to set of three states pulse-cord train relative to the original ECG signal. The pulse-cord train has the advantage of easy implementation in digital hardware circuits to achive automated ECG diagnosis. The algorithm performs very well feature extraction in arrythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contration) detection has a at least 90 percent sensityvity for arrythmia data.

• Journal of Digital Convergence
• /
• v.13 no.10
• /
• pp.313-318
• /
• 2015

A traditional speech enhancement methods distort the sound spectrum generated according to estimation of the remaining noise, or invalid noise is a problem of lowering the speech recognition performance. In this paper, we propose a speech detection method that convergence the sound energy distribution process and sound energy parameters. The proposed method was used to receive properties reduce the influence of noise to maximize voice energy. In addition, the smaller value from the feature parameters of the speech signal The log energy features of the interval having a more of the log energy value relative to the region having a large energy similar to the log energy feature of the size of the voice signal containing the noise which reducing the mismatch of the training and the recognition environment recognition experiments Results confirmed that the improved recognition performance are checked compared to the conventional method. Car noise environment of Pause Hit Rate is in the 0dB and 5dB lower SNR region showed an accuracy of 97.1% and 97.3% in the high SNR region 10dB and 15dB 98.3%, showed an accuracy of 98.6%.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.46 no.1
• /
• pp.28-33
• /
• 2009

In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. The conventional methods for speech endpoint detection are based on two simple time-domain measurements-short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Accordingly, this algorithm is low complexity and suitable for Digital Isolated Word Recognition System.

• The Journal of the Acoustical Society of Korea
• /
• v.14 no.2
• /
• pp.50-61
• /
• 1995

A time scale modification method yielding rate-modified speech while conserving the characteristic of speech was implemented in real-time using a goneral purpose digital signal processor. Time scale modification changed pronunciation speed only, producing a time difference between the input signal and the modified signal, making it impossible to implement it in real-time. In this thesis, a system was implemented to remove the time difference between the input and modified signals. Speech signals slowed down or speeded up by a physical time scale modification method, such as adjusting the motor speed of the cassett tape recorder, was used as the input signal. Physical modification that controled only the inter speed of the cassette tape player distorted the pitch period of the original speech. In this study, a real-time system was implemented so that the pitch-distorted speech was reconstructed back to the original by fractional sampling pitch shifting using an FIR filter, and this signal was time scale modified to match the cassette tape recorder motor speed using SOLA time-scale medification. In experiments using speech signals medifiedby the proposed method, results obtained using a 16-bit resolution ADSP2101 processor and using computer simulations employing floating point operations showed about the same average frame signal-to-noise ratio of about 20 dB.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.14 no.6
• /
• pp.642-649
• /
• 1989

In this paper the theory of digital filters which can directly process the ADM encoded signal, and their structures are studied. In order to investigate the frequency characteristics of DM filters with the strucutures presented in this paper, a sampled speech signal is used for the input data. The result of computer simulation shows that the presented DM filter structures can be used effectively for the direct process of ADM encoded signals, ven though they do not posses sufficiently sharp cutoff characteristics.

• Proceedings of the KIEE Conference
• /
• 1993.07a
• /
• pp.453-455
• /
• 1993

In this research, the digital signal processing PC board which employs TI's TMS320C25 is implemented. The board can perform following functions. spectrum analysis of speech and repetitive signal, digital filters emulation by convolution, signal generation of sinusoidal wave, rectangular wave etc.. In this system, communications between PC and DSP board. program down-loading to DSP board and recording and graphic of acquired and processed data in DSP board are executed by PC. Parallel interface and buffer memory are used in communications. Data acquisition and operation are carried out in DSP board. Resultant data are transmitted to PC and output through DAC.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.2 no.2
• /
• pp.37-43
• /
• 2009

In this study, multi-channel digital hearing aid algorithm for low power system is proposed. First, MDCT(Modified Discrete Cosine Transform) method converts time domain of input speech signal into frequency domain of it. Output signal from MDCT makes a group about each channel, and then each channel signal adjusts a gain using LCF(Loudness Compensation Function) table depending on hearing loss of an auditory person. Finally, compensation signal is composed by TDAC and IMDCT. Its all of process make progress 16-bit fixed-point operation. We use fast-MDCT instead of MDCT for reducing system complexity and previously computed tables instead of log computation for estimating a gain. This algorithm evaluate through computer simulation.

• Journal of Korea Multimedia Society
• /
• v.13 no.9
• /
• pp.1322-1328
• /
• 2010

Recently, keyword spotting system is greatly in the limelight as UI(User Interface) technology of ubiquitous home network system. Keyword spotting system is vulnerable to non-stationary noises such as TV, radio, dialogue. Especially, speech recognition rate goes down drastically under the embedded DSP(Digital Signal Processor) environments because it is relatively low in the computational capability to process input speech in real-time. In this paper, we propose a new keyword spotting system using the call-command method, which is consisted of small number of recognition networks. We select the call-command such as 'narae', 'home manager' and compose the small network as a token which is consisted of silence with the noise and call commands to carry the real-time recognition continuously for input speeches.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.2
• /
• pp.51-58
• /
• 1993

This paper describes the development of a speaker-dependent isolated word recognizer as applied to voice dialing in a car noise environment. for this purpose, several methods to improve performance under such condition are evaluated using database collected in a small car moving at 100km/h The main features of the recognizer are as follow: The endpoint detection error can be reduced by using the magnitude of the signal which is inverse filtered by the AR model of the background noise, and it can be compensated by using variants of the DTW algorithm. To remove the noise, an autocorrelation subtraction method is used with the constraint that residual energy obtainable by linear predictive analysis should be positive. By using the noise rubust distance measure, distortion of the feature vector is minimized. The speech recognizer is implemented using the Motorola DSP56001(24-bit general purpose digital signal processor). The recognition database is composed of 50 Korean names spoken by 3 male speakers. The recognition error rate of the system is reduced to 4.3% using a single reference pattern for each word and 1.5% using 2 reference patterns for each word.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.4
• /
• pp.857-866
• /
• 2009

In this paper, a noise cancellation-method using microphone array for digital hearing aids is proposed. The microphone array is located around the ear of a dummy. Speech sound is generated from the forward speaker positioned in the front of the dummy and noise sound is generated from the backward speaker. The speech and noise are mixed in the air space and entered into the microphones. VAD(voice activity detector) and ANC(adaptive noise cancellation) methods were used to eliminate noise in the sound of the microphones. 10 two-syllable words and 4 sentences were used for speech signals. Babble and car interior noise were used for noise signals. The performance of the proposed algorithm was evaluated by SNR(signal-to-noise ratio) and PESQ-MOS(perceptual evaluation of speech quality-mean opinion score). In babble noise condition, SNR was improved as much as $7.963{\pm}1.3620dB\;and\;3.968{\pm}0.6659dB$ for words and sentences respectively. In the case of car interior noise, SNR was improved as $10.512{\pm}2.0665dB\;and\;6.000{\pm}1.7642dB$ for words and sentences respectively. PESQ-MOS of the babble noise was improved as much as $0.1722{\pm}0.0861$ score for words and $0.083{\pm}0.0417$ score for sentences. And PESQ-MOS of the car interior noise was improved as $0.2661{\pm}0.0335$ score and $0.040{\pm}0.0201$ score for words and sentences respectively. It is verified that the proposed algorithm has a good performance in noise cancellation of microphone array for digital hearing aids.