Creation of a New Domain and Evaluation of Comparison Generation in a Natural Language Generation System

We describe the creation of a new domain for the Methodius Natural Language Generation System, and an evaluation of Methodius ’ parameterized comparison generation algorithm. The new domain was based around music and performers, and texts about the domain were generated using Methodius. Our evaluation showed that test subjects learned more from texts that contained comparisons than from those that did not. We also established that the comparison generation algorithm could generalize to the music domain.

Solemate: A Music App for Runners

Solemate is a mobile application designed to enhance the running experience through music. Our feed-forward algorithm sets the runner’s pace by playing music that varies in tempo. By encouraging the user to match their steps to the beat, our application cultivates a run that feels natural and inspires intrinsic motivation, especially for the beginner runner

Performance of Angle of Arrival Detection Using MUSIC Algorithm in Inter-Satellite Link

An attitude of satellite is not always static, sometimes it moves randomly and the antenna pointing of satellite is harder to achieve line of sight communication to other satellite when it is outage by tumbling effect. In order to determine an appropriate direction of satellite antenna in inter-satellite link, this paper analyze estimation performance of the direction of arrival (DoA) using MUSIC algorithm from connected satellite signal source. It differs from optical measurement, magnetic field measurement, inertial measurement, and global positioning system (GPS) attitude determination. The proposed method is characterized by taking signal source from connected satellites, after that the main satellite processed the information to obtain connected satellites antenna direction. The simulation runs only on the direction of azimuth. The simulation result shows that MUSIC algorithm processing time is faster than satellite movement time in orbit on altitude of 830 km with the period of 101 minutes. With the use of a 50 elements array antenna in spacing of 0.5 wavelength, the total of 20 angle of arrival (AoA) can be detected in 0.98 seconds of processing time when using MUSIC algorithm

Subspace Methods for Joint Sparse Recovery

We propose robust and efficient algorithms for the joint sparse recovery problem in compressed sensing, which simultaneously recover the supports of jointly sparse signals from their multiple measurement vectors obtained through a common sensing matrix. In a favorable situation, the unknown matrix, which consists of the jointly sparse signals, has linearly independent nonzero rows. In this case, the MUSIC (MUltiple SIgnal Classification) algorithm, originally proposed by Schmidt for the direction of arrival problem in sensor array processing and later proposed and analyzed for joint sparse recovery by Feng and Bresler, provides a guarantee with the minimum number of measurements. We focus instead on the unfavorable but practically significant case of rank-defect or ill-conditioning. This situation arises with limited number of measurement vectors, or with highly correlated signal components. In this case MUSIC fails, and in practice none of the existing methods can consistently approach the fundamental limit. We propose subspace-augmented MUSIC (SA-MUSIC), which improves on MUSIC so that the support is reliably recovered under such unfavorable conditions. Combined with subspace-based greedy algorithms also proposed and analyzed in this paper, SA-MUSIC provides a computationally efficient algorithm with a performance guarantee. The performance guarantees are given in terms of a version of restricted isometry property. In particular, we also present a non-asymptotic perturbation analysis of the signal subspace estimation that has been missing in the previous study of MUSIC.Comment: submitted to IEEE transactions on Information Theory, revised versio

Non-Negative Tensor Factorization Applied to Music Genre Classification

Music genre classification techniques are typically applied to the data matrix whose columns are the feature vectors extracted from music recordings. In this paper, a feature vector is extracted using a texture window of one sec, which enables the representation of any 30 sec long music recording as a time sequence of feature vectors, thus yielding a feature matrix. Consequently, by stacking the feature matrices associated to any dataset recordings, a tensor is created, a fact which necessitates studying music genre classification using tensors. First, a novel algorithm for non-negative tensor factorization (NTF) is derived that extends the non-negative matrix factorization. Several variants of the NTF algorithm emerge by employing different cost functions from the class of Bregman divergences. Second, a novel supervised NTF classifier is proposed, which trains a basis for each class separately and employs basis orthogonalization. A variety of spectral, temporal, perceptual, energy, and pitch descriptors is extracted from 1000 recordings of the GTZAN dataset, which are distributed across 10 genre classes. The NTF classifier performance is compared against that of the multilayer perceptron and the support vector machines by applying a stratified 10-fold cross validation. A genre classification accuracy of 78.9% is reported for the NTF classifier demonstrating the superiority of the aforementioned multilinear classifier over several data matrix-based state-of-the-art classifiers

Automatic transcription of Turkish makam music

In this paper we propose an automatic system for transcribing/nmakam music of Turkey. We document the specific/ntraits of this music that deviate from properties that/nwere targeted by transcription tools so far and we compile/na dataset of makam recordings along with aligned microtonal/nground-truth. An existing multi-pitch detection algorithm/nis adapted for transcribing music in 20 cent resolution,/nand the final transcription is centered around the/ntonic frequency of the recording. Evaluation metrics for/ntranscribing microtonal music are utilized and results show/nthat transcription of Turkish makam music in e.g. an interactive/ntranscription software is feasible using the current/nstate-of-the-art.This work is partly supported by the European/nResearch Council under the European Union’s Seventh/nFramework Program, as part of the CompMusic project/n(ERC grant agreement 267583)

Comparison Of Modified Dual Ternary Indexing And Multi-Key Hashing Algorithms For Music Information Retrieval

In this work we have compared two indexing algorithms that have been used to index and retrieve Carnatic music songs. We have compared a modified algorithm of the Dual ternary indexing algorithm for music indexing and retrieval with the multi-key hashing indexing algorithm proposed by us. The modification in the dual ternary algorithm was essential to handle variable length query phrase and to accommodate features specific to Carnatic music. The dual ternary indexing algorithm is adapted for Carnatic music by segmenting using the segmentation technique for Carnatic music. The dual ternary algorithm is compared with the multi-key hashing algorithm designed by us for indexing and retrieval in which features like MFCC, spectral flux, melody string and spectral centroid are used as features for indexing data into a hash table. The way in which collision resolution was handled by this hash table is different than the normal hash table approaches. It was observed that multi-key hashing based retrieval had a lesser time complexity than dual-ternary based indexing The algorithms were also compared for their precision and recall in which multi-key hashing had a better recall than modified dual ternary indexing for the sample data considered.Comment: 11 pages, 5 figure