An overview of speech endpoint detection algorithms

Field-Applicable Pig Anomaly Detection System Using Vocalization for Embedded Board Implementations

“…Various sounds produced by pigs were collected using the audio sensor installed in the pigpen and then transmitted to the preprocessing module where the end point detector was employed to detect the area where sound is present in the signal. In general, traditional techniques using the time domain or frequency domain characteristics of a signal have a low sound region detection rate performance when a strong signal to noise ratio (SNR) is present in the sound signal [19]. In addition, they are highly vulnerable to background noise in the case of threshold-based end point detection [19].…”

“…In general, traditional techniques using the time domain or frequency domain characteristics of a signal have a low sound region detection rate performance when a strong signal to noise ratio (SNR) is present in the sound signal [19]. In addition, they are highly vulnerable to background noise in the case of threshold-based end point detection [19]. However, in this study, pig sounds had to be acquired from pigpens where various environmental noises (such as the footsteps of pigs and the music played inside pigpens) were constantly present.…”

“…In this study, to detect sounds inside piggeries, we applied a VAD [18] algorithm that used a deep learning-based pattern-matching algorithm and guaranteed good and noise-robust sound detection performance. This VAD model was implemented based on ACAM, and the introduction of the attention technique further improved the sound detection performance, even in noisy situations [19][20][21]. In the VAD system, during the initialization process, the algorithm first converts the sound signal into overlapped frame information with 25 ms and 10 ms shifts, and then, adds context information before inputting the signal to the decoder.…”

“…In this study, we propose a low-cost real-time sound-based pig abnormality monitoring system that can be installed in real pigpens and operate for 24 h a day in an embedded environment that has limited computing resources, without needing a personal computer (PC) environment. First, the system employs an adaptive context attachment model (ACAM)-based noise-robust voice activity detection (VAD) algorithm, which can effectively detect sound regions even in noisy environments, to detect the sound regions in the data received from the sound sensor [18][19][20][21]. Then, each detected sound region is converted into a spectrogram, containing both frequency and time information, before feeding it to the lightweight deep learning model, MnasNet [22].…”

Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold

“…Due to the principle of counting the number of zerocrossing values in several fixed time blocks of the data stream, it is more sensitive to high-frequency signals and loses low-frequency signals. A real-time signal detection based on an STE crossing level algorithm with an average accuracy of 84.4% was implemented in [18], and a dual-threshold method combined by STE and ZCR with an average accuracy of 76.45% was presented in [19]. Both predefined thresholds are set by the environmental noise at the initial moment, which leads to inaccurate detection for the varying noise.…”