A Novel Techniques for Classification of Musical Instruments

Musical instrument classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. Signal is subjected to wavelet decomposition. A suitable wavelet is selected for decomposition. In our work for decomposition we used Wavelet Packet transform. After the wavelet decomposition, some sub band signals can be analyzed, particular band can be representing the particular characteristics of musical signal. Finally these wavelet features set were formed and then musical instrument will be classified by using suitable machine learning algorithm (classifier). In this paper, the problem of classifying of musical instruments is addressed.  We propose a new musical instrument classification method based on wavelet represents both local and global information by computing wavelet coefficients at different frequency sub bands with different resolutions. Using wavelet packet transform (WPT) along with advanced machine learning techniques, accuracy of music instrument classification has been significantly improved. Keywords: Musical instrument classification, WPT, Feature Extraction Techniques, Machine learning techniques

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

Audio Inpainting

(c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published version: IEEE Transactions on Audio, Speech and Language Processing 20(3): 922-932, Mar 2012. DOI: 10.1090/TASL.2011.2168211

Audio Content-Based Music Retrieval

The rapidly growing corpus of digital audio material requires novel retrieval strategies for exploring large music collections. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. In this contribution, we discuss content-based retrieval strategies that follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. Such strategies can be loosely classified according to their "specificity", which refers to the degree of similarity between the query and the database documents. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Furthermore, we introduce a second classification principle based on "granularity", where one distinguishes between fragment-level and document-level retrieval. Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise "audio identification", "audio matching", and "version identification". For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework

Deep Image Features in Music Information Retrieval

Applications of Convolutional Neural Networks (CNNs) to variousproblems have been the subject of a number of recent studiesranging from image classification and object detection to scene parsing, segmentation 3D volumetric images and action recognition in videos. In this study, the CNNs were applied to a Music Information Retrieval (MIR), in particular to musical genre recognition.The model was trained on ILSVRC-2012 (more than 1 million natural images) to perform image classification and was reused to perform genre classification using spectrograms images. Harmonic and percussion separation was applied, because it is characteristic formusical genre.At final stage, the evaluation of various strategies of merging Support Vector Machines (SVMs) was performed on well known in MIR community - GTZAN dataset.Even though, the model was trained on natural images, the results achieved in this studywere close to the state-of-the-art.

Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014