50 results about "Spectral subtraction" patented technology

The invention relates to a pipeline leakage detection method based on acoustic signal spectrum noise reduction, and belongs to the technical field of pipeline leakage monitoring. The method comprises the steps that pure background noise signals are collected through an acoustic sensor to serve as noise analysis samples, noise power spectrum estimation is conducted, and average noise power spectrum estimation is obtained; collecting an original acoustic signal containing potential leakage; performing spectrum analysis and phase reservation on the original acoustic signal after framing processing; a continuous noise reduction acoustic signal is output through spectral subtraction operation and signal reconstruction; performing feature extraction on the continuous noise reduction acoustic signal, performing anomaly detection on a current signal segment through a pipeline leakage detection model, and outputting a leakage signal segment; and leakage point positioning is carried out according to the time difference of the leakage signal segment reaching different sensors. Efficient and accurate detection of pipeline leakage is realized, and interference of background noise on a detection result is effectively overcome.

A sensorless method for machine tool chatter detection. A motor torque signal is analyzed in the time domain to determine whether a bit is currently cutting workpiece material. When not cutting material, an air-cut reference signal is stored for later use. When cutting material, the motor torque signal is converted to the frequency domain and filtered in a multi-step process. After removal of the air-cut reference signal via spectral subtraction, and removal of spindle harmonic components, additional filtering is performed to address aliasing and encoder error effects. The aliasing filtering removes artificial peaks in the frequency response spectrum resulting from interaction between sampling frequency and cutting frequency. The encoder error filtering removes frequency response peaks related to encoder design and interaction with motor speed. After filtering, indicator criteria are evaluated to detect chatter, and corrective action is taken when chatter is detected.

The invention relates to the technical field of voice communication, in particular to a voice signal processing method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: performing real-time noise classification on a received original voice signal based on a preset deep neural network noise recognition model; according to the noise classification result, selecting a corresponding combined noise reduction model to perform multi-stage noise reduction processing on the original voice signal to obtain a noise-reduced voice signal, the combined noise reduction model comprising one or more of a deep neural network-based noise reduction module, a Wiener filtering module and a spectral subtraction module; and on the basis of time domain feature extraction and frequency domain feature extraction, voice feature enhancement processing is performed on the noise-reduced voice signal to obtain the target voice signal, so that the transmission quality of the voice signal is improved.

A sensorless method for machine tool chatter detection. When the machine tool spindle is running, a spindle motor torque signal is analyzed in the time domain to determine whether a bit is currently cutting a workpiece. When not cutting, an air-cut reference signal is stored for later use. When cutting, the spindle motor torque signal, along with positioning servo motor signals, are converted to the frequency domain and filtered. Filtering steps include removal of the air-cut reference signal via spectral subtraction, removal of spindle harmonic components, removal of artificial peaks due to aliasing effects, and removal of artificial peaks due to encoder error effects. After filtering, indicator criteria are evaluated to detect chatter, including a magnitude of the filtered torque signal for servo data and a magnitude ratio of the filtered torque signal to the air-cut reference signal for spindle data. Corrective action is taken when chatter is detected.

The invention relates to the technical field of agricultural travel services, and provides a dialect intelligent customer service and culture knowledge base system, the system comprises a perception layer, an analysis layer, a knowledge layer and an interaction layer four-dimensional architecture, the perception layer collects and preprocesses dialect voice, noise reduction is performed through spectral subtraction, and Mel frequency cepstrum coefficient features are extracted; the analysis layer identifies dialects based on a fine tuning Wav2Vec2.0 model, converts the dialects into mandarin through a Transform architecture, and completes intention identification and slot filling by using a BERT related model; the knowledge layer constructs a local culture knowledge graph containing non-abandoned, folk and other entities, and supports dynamic updating; and the interaction layer is combined with multiple rounds of dialogue management to generate multiform responses, and can be connected with an external service system. The system also optimizes a feedback module iterative model and knowledge. The system can cover more than ten dialects, realizes dialect interaction, culture interpretation and service conversion closed loop, and assists rural culture revitalizing and rural cultural travel service upgrading.

The invention discloses a weak fault sound source monitoring and early warning method based on deep learning and an acoustic array, and belongs to the technical field of equipment state monitoring and fault diagnosis. In order to solve the problem of early fault detection caused by easy submergence of weak fault voiceprint signals, incomplete feature extraction and scarcity of fault samples in a strong noise environment, the method comprises the following steps: collecting multichannel signals through an acoustic array, establishing a dynamic noise baseline by adopting a Gaussian mixture model, and performing noise suppression in combination with a spectral subtraction method; extracting a composite feature vector containing time domain, frequency domain and time-frequency domain features; and carrying out fault identification by using the pre-trained and fine-tuned transfer learning model, and finally outputting an early warning according to an identification result. The method is suitable for online monitoring and early warning of early weak faults of industrial equipment.

The invention provides a Transform-based voice correction system and method, and the system comprises a voice preprocessing module which is used for carrying out the noise suppression and feature extraction of an input southern Fujian dialect voice signal, carrying out the noise suppression through a frequency spectrum subtraction method, and extracting 40-dimensional MFCC features; the voice splicing module is used for solving the problem of voice input fragmentation; the dialect speech recognition module adopts an improved Transform encoder structure; and the character correction module adopts a character level Transform language model. According to the invention, more accurate and smooth voice interaction experience is provided for the user in the dialect area in southern Fujian. Meanwhile, the technical framework has good expandability, can adapt to other dialects and language variants, and provides important support for industrial application of the dialect voice technology.

The invention discloses an AI-driven vehicle-mounted sound field real-time modeling and voice separation method, and relates to the technical field of voice signal processing. A main control unit comprising a time sequence synchronizer, a resource scheduler and a health monitor is constructed. 3D sound field modeling is carried out by adopting a lightweight STCN + bidirectional LSTM network, adaptive updating of the model is realized through EWC incremental learning, and a CNN-LSTM noise classification network and targeted suppression algorithms such as ANF / spectral subtraction are developed. The voice separation module adopts an improved Conv-TasNet architecture, 3D spatial constraint and a multi-task loss function are fused, and low delay is realized under INT8 quantization and pipeline processing. The system dynamically optimizes parameters through a real-time regulation and control unit, supports scene self-adaption, finally achieves a separation effect in a mixed noise scene, reduces the delay of the whole system, and effectively improves the definition and stability of vehicle-mounted voice interaction.

The invention relates to the technical field of artificial intelligence, and discloses a BCM module cooperative control method based on voice instruction recognition in a vehicle-mounted high-noise environment, and the method comprises the following steps: collecting original voice instruction data and vehicle state parameter data in the vehicle-mounted high-noise environment; performing voice signal preprocessing in a noise environment based on the original voice instruction data to generate de-noised voice instruction data; according to the method, the engine noise and the wind noise steady-state background noise are filtered out through multi-level noise suppression processing combining adaptive filtering and spectral subtraction, residual noise elimination is carried out for sudden impact noise, and the definition of voice signals is improved. Meanwhile, through a voice activity detection algorithm, a detection threshold is dynamically adjusted according to vehicle state parameters, an effective voice segment and a noise segment can be separated, the accuracy of voice feature extraction in a complex time-varying noise environment is ensured, and thus the robustness and reliability of voice instruction recognition are improved.

The application discloses a consumer complaint response method and system based on AI assistance and language understanding, which splits the complaint response process into two core sub-problems of multi-modal consumer complaint data processing and feature fusion and demand attribution and response generation. In multi-modal consumer complaint data processing and feature fusion, first, regular expressions are used to denoise text, spectral subtraction is used to denoise voice, and Gaussian filtering and adaptive histogram equalization are used to denoise images; then, modal features are extracted, text is used as the core of cross-modal fusion, and entity and relationship are extracted to construct a multi-modal semantic knowledge graph. In demand attribution and response generation, Graph Transformer is used in combination with the graph and domain prior knowledge to output primary and secondary demands; a static complaint graph is constructed, an attribution path is mined through BFS and is verified through multi-modal verification; and an "emotion-demand-attribution-prevention" structure is used to optimize text and adjust the format by using LLM, and an individualized complaint response is output.

This invention discloses a method for accurate audio warning identification based on hybrid features. This method analyzes collected audio speech to determine the issuance time of the warning signal, thereby accurately evaluating the timeliness of the audio warning. First, a double noise reduction method using logmmse-spectral subtraction is employed to filter out noise information in the recorded speech. Next, endpoint detection based on short-time energy is used to mark the effective speech segments in the test speech. Then, MFCC features and waveform polynomial features are extracted from each frame of the effective speech segment. Subsequently, the two features are used as inputs to two channels of a convolutional neural network, and the outputs of the two channels are summed to obtain the hybrid features. Finally, the hybrid features are used as input to a softmax function, and the speech segment containing the target speech (audio warning signal) is determined by the maximum probability value. The starting position of this speech segment is the issuance time of the warning signal.

The application relates to the technical field of artificial intelligence, and discloses a BCM module cooperative control method based on voice instruction recognition in a vehicle high-noise environment, which comprises the following steps: collecting original voice instruction data and vehicle state parameter data in the vehicle high-noise environment; performing voice signal preprocessing in the noise environment based on the original voice instruction data to generate denoised voice instruction data; through multi-level noise suppression processing combining adaptive filtering and spectral subtraction, engine noise, wind noise and steady-state background noise are filtered out, and residual noise is eliminated for sudden impact noise, so that the intelligibility of the voice signal is improved. Meanwhile, through a voice activity detection algorithm, and by dynamically adjusting the detection threshold according to the vehicle state parameters, effective voice segments and noise segments can be separated, the accuracy of voice feature extraction in a complex time-varying noise environment is ensured, and the robustness and reliability of the voice instruction recognition are improved.

The invention relates to the technical field of voice enhancement, in particular to an audio noise reduction method and system for a Bluetooth headset, and the method comprises the steps: analyzing the feature condition of noise influence in a mobile scene, including the feature change condition of a signal obtained by a microphone under the condition of pedestrian voice noise interference; according to the method, the spectral subtraction factor of the spectral subtraction method is adjusted in a targeted manner by integrating the characteristic difference conditions of the audios obtained by the reference microphone and the main microphone when the audios are moved to different scenes, so that the filtering error occurring during the audio noise reduction of the Bluetooth headset in the moving scene is avoided, and the audio noise reduction level in the call process of the Bluetooth headset is further improved.

The invention discloses a distributed reconnaissance tool, relates to the technical field of voice interception, and aims to solve the problems of limited multi-node access capability, poor anti-interference performance and insufficient multi-target interception continuity of an existing reconnaissance system. The tool comprises a micro audio node network and a base station unit, a recording module, a wireless transmission module and an encryption module are arranged in the miniature audio node, local recording and remote transmission are supported, and the miniature audio node has the characteristics of water resistance and low power consumption; the base station unit adopts an orthogonal graph division multiple access technology, at most 32 micro audio nodes can be accessed, four paths of voice can be monitored in real time, the audio quality is optimized through a spectral subtraction voice enhancement algorithm, and WIFI / Ethernet connection and time synchronization and combination of multi-node recording files are also supported. According to the invention, approaching interception and multi-target centralized deployment and control of the moving target are realized, the stability, expansibility and practicability of the interception system are improved, and the method is suitable for scenes such as public security technical investigation and the like.

The invention provides a real-time speech enhancement method and system based on dual-stage spectral subtraction and dual-mask fusion, and relates to the technical field of speech signal processing, and the method comprises the steps: respectively carrying out the framing, windowing and short-time Fourier transform of a left channel mixed signal and a right channel noise reference signal, obtaining a complex frequency spectrum and an amplitude spectrum of the left channel signal and an amplitude spectrum of the right channel noise reference signal; and performing noise estimation by adopting a first noise multiplication factor based on the amplitude spectrum of the right channel noise reference signal to obtain a noise estimation spectrum, and performing constraint spectrum subtraction on the amplitude spectrum of the left channel signal to obtain voice amplitude estimation of a first stage. According to the method, effective suppression of TTS noise and real-time speech enhancement are realized through framing windowing and frequency domain conversion in combination with over-estimation spectrum subtraction and double-mask fusion through two-stage gain application and time domain reconstruction.

The invention relates to the field of float glass production, in particular to a method and a device for identifying glass fragmentation sound in an annealing kiln. According to the method, a plurality of acoustic sensors which are arranged on a longitudinal steel beam on the non-transmission side of the annealing kiln and can tolerate the high temperature of 100 DEG C or above are used for collecting field environment sound signals; sequentially executing time domain preprocessing, improved spectral subtraction noise suppression, wavelet packet analysis and scale energy feature extraction to obtain a normalized feature vector; and finally, carrying out model training and real-time identification based on a hidden Markov model, and outputting whether the sound is glass fragmentation sound or not and a specific fragmentation type. The invention further provides a device for implementing the method, manual guarding can be replaced, accurate recognition and type judgment of glass fragmentation of the annealing kiln are achieved, the labor cost is reduced, manual monitoring defects are avoided, the fragmentation interval can be rapidly positioned, the device is adaptive to the high-temperature and high-noise scene of the annealing kiln, and the yield loss and the equipment production halt risk are reduced.

This invention relates to a method for highlighting spike discharge data of intracranial electroencephalogram (EEG) signals. The method involves collecting background noise data from the patient's brain without neuronal discharges, preprocessing the background noise data, automatically selecting the optimal order of an autoregressive (AR) model using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), estimating the AR model coefficients using the Yul-Walker equation, and constructing a background noise model. The intracranial EEG signals to be processed are then subjected to high-pass filtering. A short-time Fourier transform (STFT) and window-based frame-by-frame processing strategy are used to subtract the spectrum of the signal from the noise. By adjusting the parameters of the spectral subtraction, noise removal and preservation of neuronal signal features are achieved.

The invention relates to the technical field of hearing aids, in particular to a digital hearing aid howling suppression method and system, and the method comprises the steps: determining an energy distribution feature value; based on the distribution of all high-energy values and derivative values of each to-be-processed signal and a preset number of to-be-processed signals before the to-be-processed signal in the frequency domain and the rate at which each peak value in each to-be-processed signal drops to an adjacent next valley value, determining the noise-containing possibility degree of each to-be-processed signal, and determining the noise-containing possibility degree of each to-be-processed signal in combination with the energy distribution characteristic value. Determining an over-subtraction index; and based on the over-subtraction index, suppressing the howling noise in the voice signal by adopting a spectral subtraction method. According to the method, the frequency domain energy distribution and the time domain oscillation characteristics are dynamically analyzed, the over-subtraction index of the spectral subtraction method is adaptively adjusted, the problem that a traditional howling suppression method is poor in adaptability is solved, and the howling suppression effect of the digital hearing aid in a complex acoustic environment is improved.

The application provides a speech enhancement method based on deep learning assisted spectral subtraction, relates to the technical field of environmental noise suppression, and aims to improve the explainability of a network in deep learning and achieve excellent noise reduction effect, comprising initial feature extraction, obtaining a log power spectrum feature and a phase feature of a noisy speech signal; obtaining an enhanced amplitude spectrum according to the log power spectrum feature of the noisy speech signal and an estimation and noise reduction network; and obtaining an enhanced time domain speech signal through inverse short-time Fourier transform according to the enhanced amplitude spectrum and the initial phase. The application has the advantages of further improving the speech enhancement quality and the explainability of the network in deep learning.

The invention relates to a non-contact fault detection method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a motor audio signal for a to-be-tested motor; performing noise suppression processing on the motor audio signal, and extracting to obtain motor audio feature information; the noise suppression processing comprises the steps of dynamically adjusting a noise threshold by utilizing self-adaptive frequency spectrum subtraction, and highlighting the characteristic frequency of the motor through harmonic enhancement processing; and based on the motor audio feature information, utilizing a pre-trained motor fault detection model to obtain a fault classification result for the to-be-detected motor. The reliability of motor fault detection can be effectively improved.

This application relates to the field of language processing technology, specifically to a brain-computer interface-based auditory stimulation enhancement and reconstruction system. The system includes: a signal acquisition module for acquiring background noise and auditory signal data, obtaining the background noise and auditory response spectra; a background noise analysis module for constructing background noise interference intensity based on the differences between various physiological electrical signals and the background noise spectrum; an over-subtraction factor adjustment module for adjusting the over-subtraction factor based on the background noise interference intensity and the overlap between strong interference physiological electrical signals and the auditory response spectrum; a spectral lower limit parameter adjustment module for determining the optimal spectral lower limit parameter by analyzing the noise performance balance under each spectral lower limit parameter after spectral subtraction; an over-subtraction factor correction module for correcting the over-subtraction factor using the optimal spectral lower limit parameter; and a signal reconstruction module for reconstructing the auditory signal sequence obtained through the spectral method. This improves the denoising effect of music noise while simultaneously reducing the influence of other noises on the auditory signal, resulting in better auditory stimulation enhancement.

The application discloses a photoacoustic signal enhancement method and device based on adaptive multi-band spectral subtraction and Wiener filtering, and belongs to the technical field of speech signal enhancement. The application divides a signal into sub-bands according to Mel scale, determines a subtraction factor and a lower limit factor by a monotone decreasing function linkage according to real-time signal-to-noise ratio of each sub-band, and makes the two factors negatively correlated with the signal-to-noise ratio, so that the denoising strength and the spectral bottom filling depth are automatically adapted; the weighted moving average of the power spectrum of adjacent sub-bands is carried out to smooth isolated spectral peaks and spectral valleys left by spectral subtraction; a residual amplitude limiting is introduced before Wiener filtering, the maximum noise residual threshold is determined based on adjacent frame statistics and limiting, and Wiener filtering is carried out by taking the data after limiting as clean signal estimation; the noise spectrum is recursively updated by a forgetting factor which is dynamically adjusted according to the signal-to-noise ratio, and is only executed in the speech inactive section. The application has an output signal-to-noise ratio improvement of more than 50% when the input is 0dB, can stably enhance without damage under high signal-to-noise ratio, and is suitable for photoacoustic speech acquisition and enhancement scenes.

An elevator abnormal sound positioning method based on local and global attention models belongs to the field of elevator detection technology, deep learning and sound source localization, and comprises the following steps: step 1, using a microphone array to collect multi-channel sound signals of an elevator in the operation process, carrying out direct current removal preprocessing on the sound signals to obtain preprocessed sound signals, and storing the preprocessed sound signals in a database; complex frequency spectrum calculation and spectral subtraction denoising are carried out on the preprocessed sound signals, and a power spectrum is calculated; 2, performing feature extraction on the sound signal: extracting a logarithmic Mel spectrum feature and a phase transformation generalized cross-correlation feature of the sound signal, and splicing the two features to obtain an input feature of the model; and step 3, inputting the input features into the GLAN model to obtain an abnormal sound detection condition and an estimation result of sound source localization. The method has high positioning precision and stability.

The invention relates to a pet training device based on voiceprint recognition. The pet training device comprises a voice acquisition module, a voice processing module and a voiceprint recognition module, wherein the voice acquisition module acquires pet sounds through a microphone array and an ADC unit at a sampling rate of 16kHz and quantization precision of 16bit; the voiceprint processing module sequentially carries out pre-emphasis and framing windowing preprocessing on the signals, combines spectral subtraction / Wiener filtering noise reduction, extracts voiceprint features containing dynamic difference through a Mel-frequency cepstrum coefficient, and carries out voiceprint recognition by using a Gaussian mixture model, an x-vector or an ECAPA-TDNN model; the execution module integrates stimulation units such as electric shock, vibration, ultrasonic waves and aromatherapy, and triggers corresponding training actions according to recognition and matching scores; the storage module is used for storing specific voiceprint characteristics of one or more pets; and the control module starts recording through the remote controller, converts the collected sound into a unique voiceprint feature and stores the unique voiceprint feature.

An array of hydrophones may be deployed in a wellbore to collect sounds that may be used to identify whether a wellbore is safe to operate. This hydrophone array may include acoustic sensors that sense noises indicative of a defect that could lead to catastrophic failure of a wellbore and other noises that may be considered unwanted background noises. Techniques of the present disclosure may classify noises indicative of a defect as being “signals of interest.” The presence of “background noise” may interfere with the collection and / or evaluation of “signals of interest.” Because of this, evaluations performed on data that includes “background noise” and “signals of interest” may result in inaccurate determinations being made regarding the safety of a wellbore. As such, systems and methods of the present disclosure are directed to improving safety of a wellbore by removing “background noise” more effectively while increasing quality of “signals of interest.”

This invention relates to the field of noise reduction technology, specifically to an AI-based intelligent noise reduction method for laser-modulated speech, comprising the following steps: Analog-to-digital conversion: A high-speed, high-precision analog-to-digital conversion module is used to convert analog data into digital signals, and noise reduction is performed only on human voices according to the usage scenario; Digital filtering: Bandpass filtering technology is used to filter out all out-of-band signals; AI intelligent processing: Spectral subtraction is used to eliminate background noise. By constructing a collaborative processing link of "high-precision analog-to-digital / digital-to-analog conversion—64th-order narrow transition band FIR filtering—dynamic spectral subtraction based on AI model library", for slow-changing and stable specific background noise such as mechanical vibration thermal noise of the laser itself and ambient optical path scattering interference, a 10-millisecond non-overlapping time slice and a joint decision mechanism of three parameters (mean, variance, and entropy) of Mel frequency cepstral coefficients are used to achieve accurate dynamic tracking of noise targets and adaptive stripping of speech signals.

This invention discloses a real-time piano timbre simulation method and system based on machine learning, relating to the field of audio signal processing technology. The method includes: data acquisition and multi-dimensional annotation, acquiring multiple types of piano audio, covering techniques and seven dynamic levels, and simultaneously acquiring information such as key presses and techniques; audio preprocessing, including pre-emphasis compensation for high frequencies, Hanning window framing, Fourier transform to frequency domain, spectral subtraction for noise reduction and normalization; multi-dimensional feature extraction, extracting static features such as MFCC and spectral parameters, dynamic features such as first- and second-order differences, and overtone structures; two-stage model training, using stacked autoencoders for dimensionality reduction; real-time parsing, filtering and converting acquired performance data into parameter sequences; timbre synthesis, where the model generates a spectrum and performs an inverse Fourier transform into a waveform; and dynamic optimization, receiving user feedback. This invention solves the problems of traditional simulation methods; the two-stage model enhances timbre coherence, dynamic control achieves low latency, and multi-scenario adaptation and feedback optimization meet specific needs.

A sensorless method for machine tool chatter detection. A motor torque signal is analyzed in the time domain to determine whether a bit is currently cutting workpiece material. When not cutting material, an air-cut reference signal is stored for later use. When cutting material, the motor torque signal is converted to the frequency domain and filtered in a multi-step process. After removal of the air-cut reference signal via spectral subtraction, and removal of spindle harmonic components, additional filtering is performed to address aliasing and encoder error effects. The aliasing filtering removes artificial peaks in the frequency response spectrum resulting from interaction between sampling frequency and cutting frequency. The encoder error filtering removes frequency response peaks related to encoder design and interaction with motor speed. After filtering, indicator criteria are evaluated to detect chatter, and corrective action is taken when chatter is detected.

The invention relates to a multi-channel voice signal noise reduction method and device. The method comprises the following steps: acquiring a multi-channel vibration signal and a pure noise signal acquired by a distributed optical fiber acoustic sensing system; performing framing processing on the multi-channel vibration signal to obtain a frequency domain complex spectrum of each frame of each channel; determining a spectral entropy and a low-frequency signal-to-noise ratio according to the pure noise signal, and performing adaptive adjustment on an over-reduction coefficient according to the spectral entropy and the low-frequency signal-to-noise ratio to obtain a target over-reduction coefficient; performing spectral subtraction operation on the first amplitude spectrum according to a target over-reduction coefficient, and performing phase correction on the first phase spectrum based on the time delay of each channel to obtain a single-channel complex spectrum of each frame, thereby effectively suppressing non-stationary noise interference and keeping signal phase consistency through self-adaptive adjustment of the over-reduction coefficient and combination of multi-channel phase correction; and performing phase optimization and superposition reduction on the single-channel complex spectrum to obtain a time domain signal after noise reduction and reconstruction, thereby improving the feature fidelity of the vibration signal through phase optimization and superposition reduction.

Techniques are disclosed for a low-power and low-complexity speech onset detector (SOD) that uses a fractional-band filter structure and spectral subtraction technique to derive sub-band energy profiles to detect the onset of speech in the presence of noise. The SOD derives the sub-band energy profiles by filtering and down-sampling a full-band input audio signal using the fractional-bandwidth filter structure, which may be a low-pass filter with a cut-off frequency that is a fraction of the full bandwidth of the input signal. The SOD flexibly estimates the average noise energy across frames and the current frame speech energy in each sub-band to track noise and speech energy levels across the frames for each of the sub-bands to determine one or more band thresholds used to detect active speech. The sub-band energy profiles leverage any separation in frequency between noise and speech to detect the onset of speech in a target signal.