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 person may use many devices capable of rendering digital content on a regular basis. For instance, the user is in front of a large TV screen, and soon he or she moves away from the TV and wants to continue the media consumption. The transfer of media content across devices needs to be handled seamlessly. In this paper, we present a phone-centric approach to improve the end-user experience in multi-device ecosystem. Together with predefined parameters, we use context information to trigger content adaptation, and generate decisions relating to the transcoding operation. We have made a preliminary UI evaluation of the system, and the system was found to be useful, although requiring fine tuning and further development.

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.