This paper compares two methods for extracting room acoustic parameters from reverberated speech and music. An approach which uses statistical machine learning, previously developed for speech, is extended to work with music. For speech, reverberation time estimations are within a perceptual difference limen of the true value. For music, virtually all early decay time estimations are within a difference limen of the true value. The estimation accuracy is not good enough in other cases due to differences between the simulated data set used to develop the empirical model and real rooms. The second method carries out a maximum likelihood estimation on decay phases at the end of notes or speech utterances. This paper extends the method to estimate parameters relating to the balance of early and late energies in the impulse response. For reverberation time and speech, the method provides estimations which are within the perceptual difference limen of the true value. For other parameters such as clarity, the estimations are not sufficiently accurate due to the natural reverberance of the excitation signals. Speech is a better test signal than music because of the greater periods of silence in the signal, although music is needed for low frequency measurement.

Electrocardiogram (ECG) reflects the activities of the human heart and reveals hidden information on its structure and behaviour. The information is extracted to gain insights that assist explanation and identification of diverse pathological conditions. This was traditionally done by an expert through visual inspection of ECGs. The complexity and tediousness of this onus hinder long-term monitoring and rapid diagnosis, computerised and automated ECG signal processing are therefore sought after. In this paper an algorithm that uses independent component analysis (ICA) to improve the performance of ECG pattern recognition is proposed. The algorithm deploys the basis functions obtained via the ICA of typical ECG to extract ICA features of ECG signals for further pattern recognition, with the hypothesis that components of an ECG signal generated by different parts of the heart during normal and arrhythmic cardiac cycles might be independent. The features obtained via the ICA together with the R-R interval and QRS segment power are jointly used as the input to a machine learning classifier, an artificial neural network in this case. Results from training and validation of the MIT-BIH Arrhythmia database shows significantly improved performance in terms of recognition accuracy. This new method also allows for the reduction of the number of inputs to the classifier, simplifying the system and increasing the real-time performance. The paper presents the algorithm, discusses the principle algorithm and presents the validation results.

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Ever increasing volumes of media content and the desire to extract information from media archives motivate the studies into semantic audio information mining. Much research in this filed concerns development of bespoke systems, in which soundtracks are exclusively classified and segmented, and a specific type of sound is recognized and analyzed. This approach however is detrimental to the complete extraction of all relevant semantic information and audio scene analysis. The current study addresses the issues of soundtracks with overlapped music, speech and ambient sounds, and explores how MARSYAS (Music Analysis, Retrieval and Synthesis for Audio Signals) can be extended to mixed and overlapped soundtrack applications. The MARSYAS has been adapted to this application by means of adopting additional speech cleaning algorithms. The proposed new system can analyze arbitrary soundtracks and timestamp the occurrence of music and speech, allowing overlaps, in the form of a "sound score" for further recognition methods to extract music score and text information. Validation tests have shown that the new system handles overlapping cases and is therefore capable of extracting more information than other existing methods.

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