Linear Prediction Coefficients Research Articles

Improving piano music signal recognition through enhanced frequency domain analysis

Feature extraction is a crucial component in the analysis of piano music signals. This article introduced three methods for feature extraction based on frequency domain analysis, namely short-time Fourier transform (STFT), linear predictive cepstral coefficient (LPCC), and Mel-frequency cepstral coefficient (MFCC). An improvement was then made to the MFCC. The inverse MFCC (IMFCC) was combined with mid-frequency MFCC (MidMFCC). The Fisher criterion was used to select the 12-order parameters with the maximum Fisher ratio, which were combined into the F-MFCC feature for recognizing 88 single piano notes through a support vector machine. The results indicated that when compared with the STFT and LPCC, the MFCC exhibited superior performance in recognizing piano music signals, with an accuracy rate of 78.03 % and an F1 value of 85.92 %. Nevertheless, the proposed F-MFCC achieved a remarkable accuracy rate of 90.91 %, representing a substantial improvement by 12.88 % over the MFCC alone. These findings provide evidence for the effectiveness of the designed F-MFCC feature for piano music signal recognition as well as its potential application in practical music signal analysis.

New research on monaural speech segregation based on quality assessment

Speech enhancement (SE) is a pivotal technology in enhancing the quality and intelligibility of speech signals. Nevertheless, when processing speech signals under conditions of high signal-to-noise ratio (SNR), conventional SE techniques may inadvertently lead to a diminution in the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). This article introduces the innovative incorporation of the Non-Intrusive Speech Quality Assessment (NISQA) algorithm into SE systems. Through the comparison of pre and post-enhancement speech quality scores, it discerns whether the speech signal under consideration warrants enhancement processing, thereby mitigating potential deterioration in PESQ and STOI. Furthermore, this study delves into the ramifications of five prevalent speech features, namely, Mel Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), Relative Spectral Trans-formed Perceptual Linear Prediction coefficients (RASTA-PLP), Amplitude Modulation Spectrogram (AMS), and Multi-Resolution Cochleagram (MRCG), on PESQ and STOI under varying noise conditions. Experimental outcomes underscore that MRCG consistently emerges as the optimal and most stable feature for STOI, while the feature yielding the highest PESQ score exhibits intricate correlations with the background noise type, SNR level, and noise compatibility with the speech signal. Consequently, we propose an SE methodology founded on quality assessment and feature selection, facilitating the adaptive selection of optimal features tailored to distinct background noise scenarios, thereby always maintain the highest caliber enhancement effect with regard to PESQ metrics.

Identification of OSAHS patients based on ReliefF-mRMR feature selection.

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a serious chronic sleep disorder. Snoring is a common and easily observable symptom of OSAHS patients. The purpose of this work is to identify OSAHS patients by analyzing the acoustic characteristics of snoring sounds throughout the entire night. Ten types of acoustic features, such as Mel-frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC) and spectral entropy among others, were extracted from the snoring sounds. A fused feature selection algorithm based on ReliefF and Max-Relevance and Min-Redundancy (mRMR) was proposed for optimal feature set selection. Four types of machine learning models were then applied to validate the effectiveness of OSAHS patient identification. The results show that the proposed feature selection algorithm can effectively select features with high contribution, including MFCC and LPC. Based on the selected top-20 features and using a support vector machine model, the accuracies in identifying OSAHS patients under the thresholds of AHI = 5,15, and 30, were 100%, 100%, and 98.94%, respectively. This indicates that the proposed model can effectively identify OSAHS patients.

Deep Learning Approaches for English-Marathi Code-Switched Detection

During a conversation, speakers in multilingual societies frequently switch between two or more spoken languages. A linguistic action known as "code-switching" particularly alters or merges two or more languages. The development of software or tools for detecting code-switching has received very little attention. This paper proposes a Deep Learning based methods for detecting code-switched English-Marathi data. These suggested methods can be applied to various applications, including phone call merging, Intelligent AI assistants, Intelligent travelling systems to assist travellers in navigation and reservations, call centres to handle customer service issues, etc. To create a system for code switch detection, our study demonstrates a detailed analysis of extracting several audio features such as the Mel-Spectrogram, Mel-frequency Cepstral Coefficient (MFCC), and Perceptual Linear Predictive coefficients (PLP). Our team's English-Marathi code-switched dataset served as the testing ground for our methodologies. Our model's accuracy was 92.99%, with 40 MFCC coefficients having energy coefficient serving as the zeroth coefficient.

Evaluation of Machine Learning Algorithms using Combined Feature Extraction Techniques for Speaker Identification

The aim of this study is to evaluate and compare machine learning algorithms when various feature extraction techniques are employed together and determine the optimal feature combinations for the models studied. The TIMIT online database was used where 5 male and 5 female non-native English speakers from five American locations were selected. Each speaker had ten 3-second utterances, totaling 500. Mel frequency cepstral coefficients (MFCC), linear predictive cepstral coefficients (LPCC), gammatone frequency cepstral coefficients (GFCC), discrete wavelet transforms (DWT) and pitch features were extracted using MATLAB and concatenated. The concatenated features were used to train and evaluate three classifier models—Random Forest (RF), Linear Discriminant Analysis (LDA), and Logistic Regression (LR)—using Python software. The results obtained showed that as the number of features combinations increased, the models’ performances improved as well. This improved performance was observed when all the cepstral features were part of the combinations. This implies that cepstral features are more robust and improve speaker identification systems. The best average score of accurate predictions of ≈ 76% for the LR model was obtained for the MGL (39 features) features combination and dropped to 70% for the highest number of feature combinations MGDLP (53 features). This indicates that more training data improves system performance, however, too much data does not translate to even better performance because the system will eventually achieve its peak performance. This information is useful for applications where limited data can present a problem.

Advancements in Speaker Recognition: Exploring Mel Frequency Cepstral Coefficients (MFCC) for Enhanced Performance in Speaker Recognition

Abstract: Speaker recognition, a fundamental capability of software or hardware systems, involves receiving speech signals, identifying the speaker present in the speech signal, and subsequently recognizing the speaker for future interactions. This process emulates the cognitive task performed by the human brain. At its core, speaker recognition begins with speech as the input to the system. Various techniques have been developed for speech recognition, including Mel frequency cepstral coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral coefficients (LPCC), Line Spectral Frequencies (LSF), Discrete Wavelet Transform (DWT), and Perceptual Linear Prediction (PLP). Although LPC and several other techniques have been explored, they are often deemed impractical for real-time applications. In contrast, MFCC stands out as one of the most prominent and widely used techniques for speaker recognition. The utilization of cepstrum allows for the computation of resemblance between two cepstral feature vectors, making it an effective tool in this domain. In comparison to LPC-derived cepstrum features, the use of MFCC features has demonstrated superior performance in metrics such as False Acceptance Rate (FAR) and False Rejection Rate (FRR) for speaker recognition systems. MFCCs leverage the human ear's critical bandwidth fluctuations with respect to frequency. To capture phonetically important characteristics of speech signals, filters are linearly separated at low frequencies and logarithmically separated at high frequencies. This design choice is central to the effectiveness of the MFCC technique. The primary objective of the proposed work is to devise efficient techniques that extract pertinent information related to the speaker, thereby enhancing the overall performance of the speaker recognition system. By optimizing feature extraction methods, this research aims to contribute to the advancement of speaker recognition technology.

Investigations on the Optimal Estimation of Speech Envelopes for the Two-Stage Speech Enhancement.

Using the source-filter model of speech production, clean speech signals can be decomposed into an excitation component and an envelope component that is related to the phoneme being uttered. Therefore, restoring the envelope of degraded speech during speech enhancement can improve the intelligibility and quality of output. As the number of phonemes in spoken speech is limited, they can be adequately represented by a correspondingly limited number of envelopes. This can be exploited to improve the estimation of speech envelopes from a degraded signal in a data-driven manner. The improved envelopes are then used in a second stage to refine the final speech estimate. Envelopes are typically derived from the linear prediction coefficients (LPCs) or from the cepstral coefficients (CCs). The improved envelope is obtained either by mapping the degraded envelope onto pre-trained codebooks (classification approach) or by directly estimating it from the degraded envelope (regression approach). In this work, we first investigate the optimal features for envelope representation and codebook generation by a series of oracle tests. We demonstrate that CCs provide better envelope representation compared to using the LPCs. Further, we demonstrate that a unified speech codebook is advantageous compared to the typical codebook that manually splits speech and silence as separate entries. Next, we investigate low-complexity neural network architectures to map degraded envelopes to the optimal codebook entry in practical systems. We confirm that simple recurrent neural networks yield good performance with a low complexity and number of parameters. We also demonstrate that with a careful choice of the feature and architecture, a regression approach can further improve the performance at a lower computational cost. However, as also seen from the oracle tests, the benefit of the two-stage framework is now chiefly limited by the statistical noise floor estimate, leading to only a limited improvement in extremely adverse conditions. This highlights the need for further research on joint estimation of speech and noise for optimum enhancement.

Comparative analysis of various feature extraction techniques for classification of speech disfluencies

Speech plays a vital role in communication, from expressing oneself, to utilizing speech-based platforms, speech is a necessity. Any disruption in speech is referred to as disfluency, and can impact one's quality of life. This paper presents an experimental study on various techniques for the detection and classification of speech disfluencies. Six different types of disfluencies are examined in this paper, namely Interjection, Sound Repetition, Word Repetition, Phrase Repetition, Revision and Prolongation (6 classes). However, this paper also goes a step further by including the clean speech signals as an added class alongside the six disfluencies, thereby making this work more robust with 7 classes. Various machine learning approaches have been investigated on the University College London Archive of Stuttered Speech (UCLASS) dataset; a standard disfluency dataset generated by University College London (UCL). Five different feature extraction techniques viz. Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients (LPCC), Gammatone Frequency Cepstral Coefficients (GFCC), Mel-filterbank energy features, and Spectrograms have been used. Comparative analysis of various classifiers shows that MFCC, GFCC, and Spectrograms achieved greater than 90% accuracy on both 6 and 7 classes with the kNN classifier. As a future scope to this study, the authors aim to focus on tackling the challenges of detecting multiple disfluencies present simultaneously in a speech sample.

Ensemble learning with speaker embeddings in multiple speech task stimuli for depression detection.

As a biomarker of depression, speech signal has attracted the interest of many researchers due to its characteristics of easy collection and non-invasive. However, subjects' speech variation under different scenes and emotional stimuli, the insufficient amount of depression speech data for deep learning, and the variable length of speech frame-level features have an impact on the recognition performance. The above problems, this study proposes a multi-task ensemble learning method based on speaker embeddings for depression classification. First, we extract the Mel Frequency Cepstral Coefficients (MFCC), the Perceptual Linear Predictive Coefficients (PLP), and the Filter Bank (FBANK) from the out-domain dataset (CN-Celeb) and train the Resnet x-vector extractor, Time delay neural network (TDNN) x-vector extractor, and i-vector extractor. Then, we extract the corresponding speaker embeddings of fixed length from the depression speech database of the Gansu Provincial Key Laboratory of Wearable Computing. Support Vector Machine (SVM) and Random Forest (RF) are used to obtain the classification results of speaker embeddings in nine speech tasks. To make full use of the information of speech tasks with different scenes and emotions, we aggregate the classification results of nine tasks into new features and then obtain the final classification results by using Multilayer Perceptron (MLP). In order to take advantage of the complementary effects of different features, Resnet x-vectors based on different acoustic features are fused in the ensemble learning method. Experimental results demonstrate that (1) MFCC-based Resnet x-vectors perform best among the nine speaker embeddings for depression detection; (2) interview speech is better than picture descriptions speech, and neutral stimulus is the best among the three emotional valences in the depression recognition task; (3) our multi-task ensemble learning method with MFCC-based Resnet x-vectors can effectively identify depressed patients; (4) in all cases, the combination of MFCC-based Resnet x-vectors and PLP-based Resnet x-vectors in our ensemble learning method achieves the best results, outperforming other literature studies using the depression speech database. Our multi-task ensemble learning method with MFCC-based Resnet x-vectors can fuse the depression related information of different stimuli effectively, which provides a new approach for depression detection. The limitation of this method is that speaker embeddings extractors were pre-trained on the out-domain dataset. We will consider using the augmented in-domain dataset for pre-training to improve the depression recognition performance further.

An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection

The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of the pathological and normal voice samples show a clear class imbalance. This study aims to develop a system for the classification of pathological and normal voices that uses efficient deep learning models based on various oversampling methods, such as the adaptive synthetic sampling (ADASYN), synthetic minority oversampling technique (SMOTE), and Borderline-SMOTE directly applied to feature parameters. The suggested combinations of oversampled linear predictive coefficients (LPCs), mel-frequency cepstral coefficients (MFCCs), and deep learning methods can efficiently classify pathological and normal voices. The balanced datasets from ADASYN, SMOTE, and Borderline-SMOTE are used to validate and evaluate the various deep learning models. The experiments are conducted using model evaluation metrics such as the recall, specificity, G, and F1 value. The experimental results suggest that the proposed voice pathology detection (VPD) system integrating the LPCs oversampled by the SMOTE and a convolutional neural network (CNN) can effectively yield the highest accuracy at 98.89% when classifying pathological and normal voices. Finally, the performances of oversampling algorithms such as the ADASYN, SMOTE, and Borderline-SMOTE are discussed. Furthermore, the performance of SMOTE is superior to conventional imbalanced data oversampling algorithms, and it can be used to diagnose pathological signals in real-world applications.

Comparative Analysis of LPC and MFCC for Male Speaker Recognition in Text-Independent Context

peech is the utmost communication medium for human beings which conveys rich and valuable information such as accent, gender, emotion and unique identity. Therefore, automatic speaker recognition can be developed based on unique characteristics of one’s speech and utilized for applications such as voice dialing, online banking, and telephone shopping to verify the identity of its users. However, retrieving salient features which are capable of identifying speakers is a challenging problem in speech recognition systems since speech contains abundant information. In this study, a total of 438 audio data obtained from speakers uttering speech in text-independent context is proposed using speech data elicited from three Malay male speakers. The performance of two popularly used feature extraction techniques namely, linear prediction coefficients (LPC) and Mel-frequency cepstral coefficients (MFCC) were compared using discriminant analysis model. Although both features yielded impressive outcomes, the MFCC features surpassed that of LPC by achieving a higher accuracy rate of 99.09%, which was 4.34% higher than the latter.

Innovative Application of Sensor Combined with Speech Recognition Technology in College English Education in the Context of Artificial Intelligence

English listening is an effective way to improve students’ English expression ability and use oral communication. However, from the current situation of English teaching, the current English teaching methods are too single, and teachers do not focus on oral training in the classroom, resulting in low efficiency of classroom teaching. On the basis of following the principles of wholeness, interaction, balance, and sustainable development of educational ecology, by enhancing the synergy of ecological elements of English speaking classroom, promoting interactive dialogue among ecological subjects, and regulating classroom behaviors, it is conducive to giving full play to the advantageous role of information technology on English speaking teaching reform and promoting its sustainable development. This paper addresses the current situation of English listening teaching, especially the problem of reduced recognition rate of spoken language in noisy environment, and the principle of using dual-sensor speech recognition system proposed. We design the speech recognition method based on recurrent neural network by acquiring the weak vibration pressure speech signal of the jaw skin and the speech signal transmitted through the air during the vocalization process through the sensor. Deep machine learning algorithm is used for speech recognition in English teaching. A reasonable frame sampling frequency is set to obtain the English speech signal, then the feature parameters representing this speech signal are obtained by linear prediction coefficients, and the speech feature vector is generated, followed by the recurrent neural network algorithm to train the speech features. In the related experiments, by comparing with the commonly used speech recognition algorithms, it is proved that the proposed algorithm English teaching speech recognition has higher accuracy and faster convergence.

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction.

In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb-Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application.

Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients.

This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec).

Autoencoders as a Characterization Technique and Aid in the Classification of Volcanic Earthquakes

Volcanic seismicity is one of the most relevant parameters for the evaluation of volcanic activity and consequently the prognosis of eruptions. Earthquakes of volcanic origin are of different classes, directly related to the physical process that generates them. The distribution of the data between classes of seismic-volcanic signals generally presents an unbalanced profile (imbalanced datasets), which can hinder the performance of the classification in machine learning models. Therefore, this research presents a characterization technique (feature extract) that, in addition to reducing the dimension of each seismic record, allows a representation of the signals with the most relevant and significant information. This work proposes the use of a Dual Feature Autoencoder (DAF), which is compared with conventional characterization techniques such as Linear Prediction Coefficients (LPC) and Principal Component Analysis (PCA). The training of the model was performed with a dataset containing volcano-tectonic earthquakes (VT), long period events (LP) and Tornillo-type events (Tor) of the Galeras volcano, one of the most active volcanoes in Colombia. The classification results reach 99% of the classification of the mentioned classes.

Music Note Feature Recognition Method based on Hilbert Space Method Fused with Partial Differential Equations

Hilbert space method is an old mathematical theoretical model developed based on linear algebra and has a high theoretical value and practical application. The basic idea of the Hilbert space method is to use the existence of some stable relationship between variables and to use the dynamic dependence between variables to construct the solution of differential equations, thus transforming mathematical problems into algebraic problems. This paper firstly studies the denoising model in the process of music note feature recognition based on partial differential equations, then analyzes the denoising method based on partial differential equations and gives an algorithm for fused music note feature recognition in Hilbert space; secondly, this paper studies the commonly used music note feature recognition methods, including linear predictive cepstral coefficients, Mel frequency cepstral coefficients, wavelet transform-based feature extraction methods and Hilbert space-based feature extraction methods. Their corresponding feature extraction processes are given.

A Preliminary Fault Detection Methodology for Abnormal Distillation Column Operations Using Acoustic Signals

The fault detection of the chemical equipment operation process is an effective means to ensure safe production. In this study, an acoustic signal processing technique and a k-nearest neighbor (k-NN) classification algorithm were combined to identify the running states of the distillation columns. This method can accurately identify various fluid flow states in distillation columns, including normal and flooding states. First, the acoustic signals were collected under normal and abnormal states in an experimental distillation column. Then, the method of dual-domain feature extraction was used to extract the features such as the energy ratio and linear prediction coefficient (LPC). Moreover, the extracted feature parameters were analyzed and compared in a general way. Finally, the k-NN model was used to classify the acoustic signals. The results show that this method had high identification accuracy and provided an important reference for further research.

Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Voice Recognition

Globally, drowsiness detection prevents accidents. Blood biochemicals, brain impulses, etc., can measure tiredness. However, due to user discomfort, these approaches are challenging to implement. This article describes a voice-based drowsiness detection system and shows how to detect driver fatigue before it hampers driving. A neural network and Gray Wolf Optimizer are used to classify sleepiness automatically. The recommended approach is evaluated in alert and sleep-deprived states on the driver tiredness detection voice real dataset. The approach used in speech recognition is mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs). The SVM algorithm has the lowest accuracy (71.8%) compared to the typical neural network. GWOANN employs 13-9-7-5 and 30-20-13-7 neurons in hidden layers, where the GWOANN technique had 86.96% and 90.05% accuracy, respectively, whereas the ANN model achieved 82.50% and 85.27% accuracy, respectively.

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment.

Dance emotion recognition is an important research direction of automatic speech recognition, especially in the robot environment. It is an important research content of dance emotion recognition to extract the features that best represent speech emotion and to construct an acoustic model with strong robustness and generalization. The dance emotion data set is small in size and high in dimension. The traditional recurrent neural network (RNN) has the problem of long-range dependence disappearance, and due to the focus on local information of convolutional neural network (CNN), the mining of potential relationships between frames in the input sequence is insufficient. To solve the above problems, this paper proposes a novel linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory (LSTM) for dance emotion recognition. In this paper, the linear prediction coefficient (LPC) and Meier frequency cepstrum coefficient (MFCC) are combined to obtain a new feature, namely the linear prediction Meier frequency cepstrum coefficient (LPMFCC). Then, the combined feature obtained by combining LPMFCC with energy feature is used as the extracted dance feature. The extracted features are input into the bidirectional LSTM network for training. Finally, support vector machine (SVM) is used to classify the obtained features through the full connection layer. Finally, we conduct experiments on public data sets and obtain the better effectiveness compared with the state-of-art dance motion recognition methods.

Speech-based gender recognition using linear prediction and mel-frequency cepstral coefficients

Gender discrimination and awareness are essentially practiced in social, education, workplace, and economic sectors across the globe. A person manifests this attribute naturally in gait, body gesture, facial, including speech. For that reason, automatic gender recognition (AGR) has become an interesting sub-topic in speech recognition systems that can be found in many speech technology applications. However, retrieving salient gender-related information from a speech signal is a challenging problem since speech contains abundant information apart from gender. The paper intends to compare the performance of human vocal tract-based model i.e., linear prediction coefficients (LPC) and human auditory-based model i.e., Mel-frequency cepstral coefficients (MFCC) which are popularly used in other speech recognition tasks by experimentation of optimal feature parameters and classifier’s parameters. The audio data used in this study was obtained from 93 speakers uttering selected words with different vowels. The two feature vectors were tested using two classification algorithms namely, discriminant analysis (DA) and artificial neural network (ANN). Although the experimental results were promising using both feature parameters, the best overall accuracy rate of 97.07% was recorded using MFCC-ANN techniques with almost equal performance for male and female classes.