Download Analysis and Trans-synthesis of Acoustic Bowed-String Instrument Recordings: a Case Study using Bach Cello Suites In this paper, analysis and trans-synthesis of acoustic bowed string instrument recordings with new non-negative matrix factorization (NMF) procedure are presented. This work shows that it may require more than one template to represent a note according to time-varying behavior of timbre, especially played by bowed string instruments. The proposed method improves original NMF without the knowledge of tone models and the number of required templates in advance. Resultant NMF information is then converted into the synthesis parameters of the sinusoidal synthesis. Bach cello suites recorded by Fournier and Starker are used in the experiments. Analysis and trans-synthesis examples of the recordings are also provided. Index Terms—trans-synthesis, non-negative matrix factorization, bowed string instrument
Download Drumkit Transcription via Convolutive NMF Audio to midi software exists for transcribing the output of a multimic’ed drumkit. Such software requires that the drummer uses multiple microphones to capture a single stream of audio for each kit piece. This paper explores the first steps towards a system for transcribing a drum score based upon the input of a single mono microphone. Non-negative Matrix Factorisation is a widely researched source separation technique. We describe a system for transcribing drums using this technique presenting an improved gains update method. A good level of accuracy is achieved on on complex loops and there are indications the mis-transcriptions are for perceptually less important parts of the score.
Download Classification Of Music Signals In The Visual Domain With the huge increase in the availability of digital music, it has become more important to automate the task of querying a database of musical pieces. At the same time, a computational solution of this task might give us an insight into how humans perceive and classify music. In this paper, we discuss our attempts to classify music into three broad categories: rock, classical and jazz. We discuss the feature extraction process and the particular choice of features that we used- spectrograms and mel scaled cepstral coefficients (MFCC). We use the texture-of- texture models to generate feature vectors out of these. Together, these features are capable of capturing the frequency-power profile of the sound as the song proceeds. Finally, we attempt to classify the generated data using a variety of classifiers. we discuss our results and the inferences that can be drawn from them.
Download Sub-Band Independent Subspace Analysis for Drum Transcription While Independent Subspace Analysis provides a means of separating sound sources from a single channel signal, making it an effective tool for drum transcription, it does have a number of problems. Not least of these is that the amount of information required to allow separation of sound sources varies from signal to signal. To overcome this indeterminacy and improve the robustness of transcription an extension of Independent Subspace Analysis to include sub-band processing is proposed. The use of this approach is demonstrated by its application in a simple drum transcription algorithm.
Download Real Time Implementation of the HVXC MPEG-4 Speech Coder In this paper we present the results of the code optimization for the HVXC MPEG-4 speech coder. Two kinds of bit-rate formats are considered: 2 and 4 kbit/s. After a short description of the HVXC main features, results of code optimization are reported: the real time implementationon, on a floating point DSP, of three parallel 2 kbit/s or two parallel 4 kbit/s HVXC coders, is shown to be possible.
Download Independent subspace analysis using locally linear embedding While Independent Subspace Analysis provides a means of blindly separating sound sources from a single channel signal, it does have a number of problems. In particular the amount of information required for separation of sources varies with the signal. This is as a result of the variance-based nature of Principal Component Analysis, which is used for dimensional reduction in the Independent Subspace Analysis algorithm. In an attempt to overcome this problem the use of a non-variance based dimensional reduction method, Locally Linear Embedding, is proposed. Locally Linear Embedding is a geometry based dimensional reduction technique. The use of this approach is demonstrated by its application to single channel source separation, and its merits discussed.
Download A hierarchical approach to automatic musical genre classification A system for the automatic classification of audio signals according to audio category is presented. The signals are recognized as speech, background noise and one of 13 musical genres. A large number of audio features are evaluated for their suitability in such a classification task, including well-known physical and perceptual features, audio descriptors defined in the MPEG-7 standard, as well as new features proposed in this work. These are selected with regard to their ability to distinguish between a given set of audio types and to their robustness to noise and bandwidth changes. In contrast to previous systems, the feature selection and the classification process itself are carried out in a hierarchical way. This is motivated by the numerous advantages of such a tree-like structure, which include easy expansion capabilities, flexibility in the design of genre-dependent features and the ability to reduce the probability of costly errors. The resulting application is evaluated with respect to classification accuracy and computational costs.
Download Implementing Loudness Models in MATLAB In the field of psychoacoustic analysis the goal is to construct a transformation that will map a time waveform into a domain that best captures the response of a human perceiving sound. A key element of such transformations is the mapping between the sound intensity in decibels and its actual perceived loudness. A number of different loudness models exist to achieve this mapping. This paper examines implementation strategies for some of the more well-known models in the Matlab software environment.
Download Simulation-Based Plate-Reverb Parameter Estimation from a Single Impulse Response ★ We present a simulation-trained, non-iterative estimator for Task A of the 1st DAFx Parameter Estimation Challenge. Each unnormalized plate-reverb impulse response is summarized by amplitude, spectral, and decay descriptors, and an ensemble of tree regressors estimates the six target parameters in one pass. Across two independent synthetic validation sets, the normalized models outperform the training-set mean and an earlier raw-regression baseline. On a shared set, the final ensemble also outperforms a single run of the official default PSO at substantially lower inference cost. Since the official labels are hidden, parameter accuracy is measured on simulator-matched data, and the released responses support only audio-side consistency checks. The estimator returns point estimates without uncertainty.
Download Effective Singing Voice Detection in Popular Music Using ARMA Filtering Locating singing voice segments is essential for convenient indexing, browsing and retrieval large music archives and catalogues. Furthermore, it is beneficial for automatic music transcription and annotations. The approach described in this paper uses Mel-Frequency Cepstral Coefficients in conjunction with Gaussian Mixture Models for discriminating two classes of data (instrumental music and singing voice with music background). Due to imperfect classification behavior, the categorization without additional post-processing tends to alternate within a very short time span, whereas singing voice tends to be continuous for several frames. Thus, various tests have been performed to identify a suitable decision function and corresponding smoothing methods. Results are reported by comparing the performance of straightforward likelihood based classifications vs. postprocessing with an autoregressive moving average filtering method.