Musical instrument identification using MFCC

Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.

While advances in sensor and signal processing techniques have provided effective tools for quantitative research on traditional Chinese pulse diagnosis (TCPD), the automatic classification of pulse waveforms is remained a difficult problem. To address this issue, this paper proposed a novel edit distance with real penalty (ERP)-based k-nearest neighbors (KNN) classifier by referring to recent progresses in time series matching and KNN classifier. Taking advantage of the metric property of ERP, we first develop a Gaussian ERP kernel, and then embed it into kernel difference-weighted KNN classifier. The proposed Gaussian ERP kernel classifier is evaluated on a dataset which includes 2470 pulse waveforms. Experimental results show that the proposed classifier is much more accurate than several other pulse waveform classification approaches.

Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach

Spectrum sensing use current primary user (PU) received signal to detect PU channel state at current time, however PU channel state in near future using previous detected channel state information can secure an efficient utilization of unoccupied spectrum, also can help in preventing harmful interference with PU and solving problem of latency between spectrum sensing and data transmission which caused by hardware implementation. In this paper, an algorithm Based on K-Nearest Neighbor (KNN) Classifier and Kalman Filter is proposed to detect and predict future of primary user channel state the spectrum holes. The new algorithm based on training KNN classifier over a set containing some frame time slots energy test statistics along with their corresponding decisions about presence or absence of primary user transmission, so as to predict decisions for new unclassified time slot, then utilize Kalman filter to track PU primary user channel state by next expected spectrum holes based on previously detected hole. Experimental results show that proposed approach is efficient and easy to implement for detecting and tracking predicting PU channel state. KEYWORD: Channel state tracking; Channel state prediction; K-nearest neighbor classifier; Kalman Filter; Spectrum sensing time slot

Detecting multiple pitches (F0s) and segregating musical instrument lines from monaural recordings of contrapuntal polyphonic music into separate tracks is a difficult problem in music signal processing. Applications include audio-to-MIDI conversion, automatic music transcription, and audio enhancement and transformation. Past attempts at separation have been limited to separating two harmonic signals in a contrapuntal duet (Maher, 1990) or several harmonic signals in a single chord (Virtanen and Klapuri, 2001, 2002). Several researchers have attempted polyphonic pitch detection (Klapuri, 2001; Eggink and Brown, 2004a), predominant melody extraction (Goto, 2001; Marolt, 2004; Eggink and Brown, 2004b), and instrument recognition (Eggink and Brown, 2003). Our solution assumes that each instrument is represented as a time-varying harmonic series and that errors can be corrected using prior knowledge of instrument spectra. Fundamental frequencies (F0s) for each time frame are estimated from input spectral data using an Expectation-Maximization (EM) based algorithm with Gaussian distributions used to represent the harmonic series. Collisions (i.e., overlaps) between instrument harmonics, which frequently occur, are predicted from the estimated F0s. The uncollided harmonics are matched to ones contained in a pre-stored spectrum library in order that each F0‘s harmonic series is assigned to the appropriate instrument. Corrupted harmonics are restored using data taken from the library. Finally, each voice is additively resynthesized to a separate track. This algorithm is demonstrated for a monaural signal containing three contrapuntal musical instrument voices with distinct timbres.KeywordsHarmonic SpectrumHarmonic SeriesMusic Information RetrievalPitch DetectionSeparate TrackThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper presents the combination of electroencephalogram (EEG) power spectrum ratio and Spectral Centroids techniques to extract unique features for human stress from EEG signals. The combination of these techniques was able to improve the k-NN (k-Nearest Neighbor) classifier accuracy to detect and classify human stress from two cognitive states, Close-eye (CE) and Open-eye (OE). The EEG power spectrum in term of Energy Spectral Density (ESD) for each frequency bands (Delta, Theta, Alpha and Beta) was calculated. The ratio of EEG power spectrum and the average value of Spectral Centroids were selected as features to k-Nearest Neighbor (k-NN). The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The results showed that the combination of EEG power spectrum and Spectral Centroids techniques with the training and testing of k-NN set at 70:30 able to detect and classify the unique features for human stress at 88.89% accuracy.

The K-Nearest Neighbour (KNN) classification is a standard method in pattern recognition and is especially marked out by its simplicity [128, 148, 305, 370, 374, 379, 389].

This paper presents automatic detection and localization of myocardial infarction (MI) using K-nearest neighbor (KNN) classifier. Time domain features of each beat in the ECG signal such as T wave amplitude, Q wave and ST level deviation, which are indicative of MI, are extracted from 12 leads ECG. Detection of MI aims to classify normal subjects without myocardial infarction and subjects suffering from Myocardial Infarction. For further investigation, Localization of MI is done to specify the region of infarction of the heart. Total 20,160 ECG beats from PTB database available on Physio-bank is used to investigate the performance of extracted features with KNN classifier. In the case of MI detection, sensitivity and specificity of KNN is found to be 99.9% using half of the randomly selected beats as training set and rest of the beats for testing. Moreover, Arif-Fayyaz pruning algorithm is used to prune the data which will reduce the storage requirement and computational cost of search. After pruning, sensitivity and specificity are dropped to 97% and 99.6% respectively but training is reduced by 93%. Myocardial Infarction beats are divided into ten classes based on the location of the infarction along with one class of normal subjects. Sensitivity and Specificity of above 90% is achieved for all eleven classes with overall classification accuracy of 98.8%. Some of the ECG beats are misclassified but interestingly these are misclassified to those classes whose location of infarction is near to the true classes of the ECG beats. Pruning is done on the training set for eleven classes and training set is reduced by 70% and overall classification accuracy of 98.3% is achieved. The proposed method due to its simplicity and high accuracy over the PTB database can be very helpful in correct diagnosis of MI in a practical scenario.

In this paper, we propose a novel framework of localization algorithm based on Compressive Sensing (CS) by K-Nearest Neighbor (KNN) classification in Wireless Sensor Networks (WSNs), which can effectively alleviate the problem of off-grid. Many papers about CS-based localization assume that each target is in the center of the grid. However, the assumption is almost impossible. In this paper, the targets can be located anywhere in the area. The target location is regarded as an unknown sparse signal in the discrete spatial domain. Practically, the recovered discrete signal is not exactly K-sparse. In order to make high level of localization accuracy, we localize the targets in three different situations and choose the cluster region according to the distribution of the power of the targets. Then for each classification, we obtain the final location by KNN. This approach can make great use of the location information and compensate the error induced by the problem of off-grid. Finally, simulation and performance analysis show the effectiveness and robustness of our proposed localization algorithm.

Abstract- This study uses a K-Nearest Neighbours (KNN) classifier to detect teenage cardiac disease. Years of age, Sexual orientation, Chest Ache category, Ambient Heart pressure, cholesterol levels, Highest Cardiac Level, and Exercise-Induced Angina are clinically important and interpretable aspects of the cardiovascular disease dataset used to create the model. These qualities were chosen to improve model interpretability for doctors and laypeople. Data preparation encoded categorical variables and standardised features for model optimisation. Histograms, charts with bars, and scatter plots were used to explore feature distribution and heart disease status. The kernel nearest neighbour (KNN) model was trained with k=11 neighbours and tested using precision, a matrix of confusion, classifying report, ROC spectrum, and precision-recall curve, proving predictive power. To select the most influential predictors, feature importance was permutation-based. A Flask web application lets users input health parameters to forecast heart disease risk. The software generates personalised, patient-friendly health information using OpenAI's API and generates PDF reports. The technology keeps prognosis records in an information system for future reference. This approach uses data-driven modelling, interpretability, and user-friendliness to help adolescents recognise and understand heart disease risk. Keywords - Adolescent heart disease identification, risk factors for heart disease, KNN classifier, artificial intelligence, data preprocessing, feature relevance, model assessment. Flask web app, OpenAI integration into the API, health insights, PDF reports, early diagnosis, and healthcare information visualisation.

Deep Learning has emerged as a powerful set of computational methods achieving great results in a variety of different tasks. Music signal processing, a field with rich commercial applications, seems to benefit too from this data-driven approach. In this paper a review of the state of the art Deep Learning methods applied on music signal processing is provided. A special focus is given in music information retrieval and music generation. In addition, possible future research directions are discussed.

Recently, computer and smartphone devices have been increasingly employed in various life aspects. Human Activity Recognition (HAR) is one of the most emerging fields that can be clearly noticed in terms of computer related researches. This study proposes a classification model to recognize human six basic activities based on smartphone embedded sensors. For feature selection, we utilized three optimization search algorithms which are: Particle Swarm Optimization (PSO), Greedy, and Genetic algorithm. K-nearest neighbors (KNN) classifier, with Manhattan distance function, is adopted to measure the models performance on two publicly available well-known datasets (i.e., WISDM and UCI-HAR). All experiments are achieved using 10-folds cross validation technique, and the results are evaluated and compared using different metrics, for instance, accuracy, root mean square error, f-measure, and number of features. The experimental results proved that PSO based KNN classifier presents the best performance compared to the other two algorithms, in addition to the baseline KNN classifier. Moreover, the proposed model outperforms those state-of-the-art models presented in related literature regarding the UCI-HAR dataset with performance rate of 98.89%, and close to the best performance achieved on WISDM dataset with performance rate of 91.07%.

PurposeThis work aimed to improve breast screening program accuracy using automated classification. The goal was to determine if whole image features represented in the discrete cosine transform would provide a basis for classification. Priority was placed on avoiding false negative findings.MethodsOnline datasets were used for this work. No informed consent was required. Programs were developed in Mathematica and, where necessary to improve computational performance ported to C++. The use of a discrete cosine transform to separate normal from cancerous breast tissue was tested. Features (moments of the mean) were calculated in square sections of the transform centered on the origin. K-nearest neighbor and naive Bayesian classifiers were tested.ResultsForty-one features were generated and tested singly, and in combination of two or three. Using a k-nearest neighbor classifier, sensitivities as high as 98% with a specificity of 66% were achieved. With a naive Bayesian classifier, sensitivities as high as 100% were achieved with a specificity of 64%.ConclusionWhole image classification based on discrete cosine transform (DCT) features was effectively implemented with a high level of sensitivity and specificity achieved. The high sensitivity attained using the DCT generated feature set implied that these classifiers could be used in series with other methods to increase specificity. Using a classifier with near 100% sensitivity, such as the one developed in this project, before applying a second classifier could only boost the accuracy of that classifier.

In this study, an effective diagnostic method for wiring networks based on reflectometry technique, the K-Nearest Neighbour (KNN) classifier, and Dynamic Time Warping (DTW) was developed. The proposed approach relies on a two-fold process: the offline process and the online process. In the offline process, basic circuit elements-based modelling and the Finite-Difference Time-Domain (FDTD) numerical method are employed to simulate Time Domain Reflectometry (TDR) and generate necessary datasets simultaneously. These datasets are then used to train and obtain classification and regression models. The DTW distance is combined with the KNN classifier to derive these models. In the online process, the models are utilised to identify, locate, and characterise faults in Wiring Networks Under Test based on their TDR response. Numerical and experimental results are presented to illustrate the performance and feasibility of the proposed method.

Violence recognition falls in the domain of action recognition which has gained considerable attention and importance due to its wide application. Violence recognition has to take place in real time. One main approach to accelerate the recognition is to efficiently choose and calculate suitable features to be used in recognition which is known as feature selection. This paper proposes the use of only nine harmonic means of relational distances between pairs of six joints and one relational velocity between 2 joints. The selected joints are chosen carefully based on having the highest information gain for the recognition. The results show that very high accuracy rate of 99.8 % can be achieved with k-nearest neighbours (k-NN) classifier. This excellent recognition rate would encourage researchers in trying to implement the proposed approach in hardware, as it uses comparatively few data for processing with simple algorithms.