In applications such as music information and database retrieval systems, classification of musical instruments plays an important role. The proposed work presents automatic classification of Indian Classical instruments based on spectral and MFCC features using well trained back propogation neural network classifier. Musical instruments such as Harmonium, Santoor and Tabla are considered for an experimentation. The spectral features such as amplitude and spectral range along with Mel Frequency Cepstrum Coefficients are considered as features. Being features are not distinguished, classification is done using non parametric classifiers such as neural networks. Being number of cepstrum coefficients are large important coefficients are selected using Principal Component Analysis. It has been observed that using 42 samples for training and 18 for testing, back propogation neural network provides accuracy of 98 % .The present work can be extended for more number of Hindustani and Carnitic classical musical Instruments.

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This is a topic that receives a lot of interest since many applications of computer vision focus on the detection of objects in visually appealing environments. Information about an object’s appearance and information regarding the object’s motion are both used as crucial signals in the process of identifying and recognising any given item. This information is used to characterise and recognise the item. The identification of objects based solely on their outward appearance has been the subject of a substantial amount of research. However, motion information in the recognition task has received only a marginal amount of attention, despite the fact that motion plays an essential role in the process of recognition. In order to analyze a moving picture in a way that is both fast and accurate, it is required to make use of motion information in conjunction with surface appearance in a strategy that has been designed. Dynamic texture is a kind of visual phenomenon that may be characterised as a type of visual phenomenon that shows spatially repeated features as well as some stationary properties during the course of time by using methodologies that are associated with machine learning. The design of modern VLSI systems takes into consideration a larger chip density, which results in a processor architecture with several cores that are capable of performing a wide range of functions (multicore processor architecture). It is becoming more challenging to run such complicated systems without the use of electric power. In order to increase the effectiveness of power optimization strategies while maintaining system performance for text data extraction, it has been developed and put into practice power optimization strategies that are based on scheduling algorithms. Over the last twenty years, texture analysis has been an increasingly busy and profitable field of study. Today, texture interpretation plays a vital role in various activities ranging from remote sensing to medical picture analysis. The absence of tools to newline analyze the many properties of texture pictures was the primary challenge faced by the texture analysis approach. Texture analysis may be roughly categorised as texture classification, texture segmentation, texture synthesis, and texture synthesis. Texture categorization is useful in numerous applications, such as the retrieval of picture databases, industrial agriculture applications, and biomedical applications. Texture categorization relies on three distinct methods, namely, statistical, spectral, and structural methods. Statistical methods are based on the statistical characteristics of the image’s grey level. Features are collected using second order statistical order, autocorrelation function, and grey level co-occurrence matrix function.

Instrument recognition in computer music is an important research area that deals with sound modelling. Musical sounds comprises of five prominent constituents which are Pitch, timber, loudness, duration, and spatialization. The tonal sound is function of all these components playing critical role in deciding quality. The first four parameters can be modified, but timbre remains a challenge [6]. Then, inevitably, timbre became the focus of this piece. It is a sound quality that distinguishes one musical instrument from another, regardless of pitch or volume, and it is critical. Monophonic and polyphonic recordings of musical instruments can be identified using this method. To evaluate the proposed approach, three Indian instruments were experimented to generate training data set. Flutes, harmoniums, and sitars are among the instruments used. Indian musical instruments classify sounds using statistical and spectral parameters. The hybrid features from different domains extracting important characteristics from musical sounds are extracted. An Indian Musical Instrument SVM and GMM classifier demonstrate their ability to classify accurately. Using monophonic sounds, SVM and Polyphonic produce an average accuracy of 89.88% and 91.10%, respectively. According to the results of the experiments, GMM outperforms SVM in monophonic recordings by a factor of 96.33 and polyphonic recordings by a factor of 93.33.