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 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 Generalised Prior Subspace Analysis for Polyphonic Pitch Transcription
A reformulation of Prior Subspace Analysis (PSA) is presented, which restates the problem as that of fitting an undercomplete signal dictionary to a spectrogram. Further, a generalization of PSA is derived which allows the transcription of polyphonic pitched instruments. This involves the translation of a single frequency prior subspace of a note to approximate other notes, overcoming the problem of needing a separate basis function for each note played by an instrument. Examples are then demonstrated which show the utility of the generalised PSA algorithm for the purposes of polyphonic pitch transcription.
Download Using tensor factorisation models to separate drums from polyphonic music
This paper describes the use of Non-negative Tensor Factorisation models for the separation of drums from polyphonic audio. Improved separation of the drums is achieved through the incorporation of Gamma Chain priors into the Non-negative Tensor Factorisation framework. In contrast to many previous approaches, the method used in this paper requires little or no pre-training or use of drum templates. The utility of the technique is shown on real-world audio examples.
Download Harmonic/Percussive Separation using Median Filtering
In this paper, we present a fast, simple and effective method to separate the harmonic and percussive parts of a monaural audio signal. The technique involves the use of median filtering on a spectrogram of the audio signal, with median filtering performed across successive frames to suppress percussive events and enhance harmonic components, while median filtering is also performed across frequency bins to enhance percussive events and supress harmonic components. The two resulting median filtered spectrograms are then used to generate masks which are then applied to the original spectrogram to separate the harmonic and percussive parts of the signal. We illustrate the use of the algorithm in the context of remixing audio material from commercial recordings.
Download Stereo Vocal Extraction Using Adress and Nearest Neighbours Median Filtering
An efficient and effective stereo vocal extraction algorithm is presented, which combines two existing approaches. A Nearest Neighbours Median Filtering algorithm is used to separate the vocals and the instrumental backing track from the stereo mixture. The separated vocal track is then passed through a mask generated by the Adress algorithm and high-pass filtered to extract the vocals. The separated instrumental backing track is then improved by adding to it the residual backing track energy extracted by Adress. Also investigated is a variant on this algorithm which uses a difference spectrogram to calculate the nearest neighbours. The effectiveness of these algorithms is then demonstrated on a test dataset, and results show that the proposed algorithms give performance comparable to the state of the art, but at a low computational cost.