The use of cepstral coefficients derived from a filter bank with logarithmically spaced band center frequencies and Gaussian mixture models (GMMs) with quantized parameters (qGMMs) are proposed for accelerometer-based activity recognition of mobile phone users. The use of a filter bank with logarithmically spaced band center frequencies is shown to yield better results than the use of a filter bank with linear spacing between band center frequencies. GMMs and qGMMs are shown to achieve similar recognition accuracies. However, the computation time using qGMMs is shown to be either at the same level or faster when compared to GMMs, depending on model complexity. Using the proposed approach, we achieve an accuracy of 72.6% and 91.3% on two recognition tasks with seven and five activities, respectively.

This paper studies the idea of using avatars as a user interface for discovering new music. In the evaluated prototype, the user builds an avatar from three parts (head, body and background). The appearance of each part reflects a certain musical genre. Based on the selected combination of parts, the application generates a new playlist of music by seeding a content-based music recommender with examples from the selected genres. In a user study with 40 participants, the prototype was considered to be entertaining and easy to use. The concept inspired users to explore new music and provided faster access to cross-genre playlists than traditional music player applications. In the longer term use, the prototype was slightly too simple and it would have benefited from, e.g., text-based search functionality. Several other interesting ideas for the future development of the concept were also received.

An approach for tempo estimation from musical pieces with near-constant tempo is proposed. The method consists of three main steps: measuring the degree of musical accent as a function of time, periodicity analysis, and tempo estimation. Novel accent features based on the chroma representation are proposed. The periodicity of the accent signal is measured using the generalized autocorrelation function, followed by tempo estimation usingk-Nearest Neighbor regression. We propose a resampling step applied to an unknown periodicity vector before finding the nearest neighbors. This step improves the performance of the method significantly. The tempo estimate is computed as a distance-weighted median of the nearest neighbor tempi. Experimental results show that the proposed method provides significantly better tempo estimation accuracies than three reference methods.

This paper presents a rhythm metadata enabled music player. The player allows the user to navigate within a music track by skipping to the next or previous measure while the playback remains continuous. The player also includes a looping functionality allowing the user to repeat the currently playing measure. The player enables the above navigation and looping in an entertaining way without breaking the sensation of beat and rhythm. We also consider an easy way of creating variations of an original music track with the help of rhythm metadata and present a data format for storing such a variation.

The aim of this paper is to investigate the feasibility of an audio-based context recognition system. Here, context recognition refers to the automatic classification of the context or an environment around a device. A system is developed and compared to the accuracy of human listeners in the same task. Particular emphasis is placed on the computational complexity of the methods, since the application is of particular interest in resource-constrained portable devices. Simplistic low-dimensional feature vectors are evaluated against more standard spectral features. Using discriminative training, competitive recognition accuracies are achieved with very low-order hidden Markov models (1-3 Gaussian components). Slight improvement in recognition accuracy is observed when linear data-driven feature transformations are applied to mel-cepstral features. The recognition rate of the system as a function of the test sequence length appears to converge only after about 30 to 60 s. Some degree of accuracy can be achieved even with less than 1-s test sequence lengths. The average reaction time of the human listeners was 14 s, i.e., somewhat smaller, but of the same order as that of the system. The average recognition accuracy of the system was 58% against 69%, obtained in the listening tests in recognizing between 24 everyday contexts. The accuracies in recognizing six high-level classes were 82% for the system and 88% for the subjects.

A method is described which analyzes the basic pattern of beats in a piece of music, the musical meter. The analysis is performed jointly at three different time scales: at the temporally atomic tatum pulse level, at the tactus pulse level which corresponds to the tempo of a piece, and at the musical measure level. Acoustic signals from arbitrary musical genres are considered. For the initial time-frequency analysis, a new technique is proposed which measures the degree of musical accent as a function of time at four different frequency ranges. This is followed by a bank of comb filter resonators which extracts features for estimating the periods and phases of the three pulses. The features are processed by a probabilistic model which represents primitive musical knowledge and uses the low-level observations to perform joint estimation of the tatum, tactus, and measure pulses. The model takes into account the temporal dependencies between successive estimates and enables both causal and noncausal analysis. The method is validated using a manually annotated database of 474 music signals from various genres. The method works robustly for different types of music and improves over two state-of-the-art reference methods in simulations.

The paper concerns the development of a system for the recognition of a context or an environment based on acoustic information only. Our system uses Mel-frequency cepstral coefficients and their derivatives as features, and continuous density hidden Markov models (HMM) as acoustic models. We evaluate different model topologies and training methods for HMMs and show that discriminative training can yield a 10% reduction in error rate compared to maximum-likelihood training. A listening test is made to study the human accuracy in the task and to obtain a baseline for the assessment of the performance of the system. Direct comparison to human performance indicates that the system performs somewhat worse than human subjects do in the recognition of 18 everyday contexts and almost comparably in recognizing six higher level categories.

In this paper, a system for pitch independent musical instrument recognition is presented. A wide set of features covering both spectral and temporal properties of sounds was investigated, and their extraction algorithms were designed. The usefulness of the features was validated using test data that consisted of 1498 samples covering the full pitch ranges of 30 orchestral instruments from the string, brass and woodwind families, played with different techniques. The correct instrument family was recognized with 94% accuracy and individual instruments in 80% of cases. These results are compared to those reported in other work. Also, utilization of a hierarchical classification framework is considered.