A hybrid method for impulse response measurements with synthesized musical tones and Masked-MLS Stimuli

Impulse responses or transfer functions are descriptions of acoustic and audio transmission channels, which completely characterise point-to-point propagation of sound in room acoustics or input-output relationships of linear systems in electroacoustics. Measurements of impulse responses or transfer functions are routinely carried out first to determine critical room acoustics parameters of enclosures such as concert halls, theatres and auditoria, or technical specification of electroacoustic transducers, i.e. loudspeakers and microphones. In room acoustics measurements, tradition techniques employ noisy testing signals as probe stimuli, which are unpleasant and intolerable to audiences. This hinders occupied measurements to be taken in many cases. Predicted in-use parameters from unoccupied measurements are known to be unreliable and problematic. It is also well appreciated in room acoustics research community that the use of musical or music-masked probe stimuli can mitigate problems of occupied measurements. It is therefore hypothesised as a starting point of this thesis that the use of musical tone like stimuli or musically masked testing signals can be used to determine impulse responses or transfer functions. Based on the above hypothesis, this thesis develops a new hybrid technique, in which narrow band linear chirps, called “presto-chirps” centred on musical notes are used to measure impulse responses in low to mid frequency bands, and music-masked maximum-length-sequences are deployed to obtain those in higher frequency bands. Broadband impulse responses are then obtained by combining the measured lower and higher frequency impulse responses. To test the hypothesis and identify the potential and limitations of the developed technique, mathematical formulation and analysis, computer simulations and real room measurements have been carried out and documented in this thesis. Investigation results show that purposely tailored and windowed narrow chirps that emulate musical tones can be used as probe stimuli to measure impulse responses or transfer functions with an uncompromised accuracy. It is found that Hanning windows are almost optimal for this application. This method covers frequency ranges commonly quoted in room acoustics investigations. Music-masked maximum-length sequences are found to be able to obtain in impulse responses or transfer functions in higher frequency. However, if completely masked stimuli are sought, the resulted signal to noise ratios in the measurements is limited, or the required averaging is going to be overly prolonged. Nevertheless, the masking music can still potentially be used as a distracter to make the audience more forgiving to the hissing noise from maximum length sequences, facilitating the occupied measurements

Reduction of wind induced microphone noise using singular spectrum analysis technique

Wind induced noise in microphone signals is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. This thesis is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling.Thethesis presents a new approach to wind noise problem. Instead of filtering, a separation technique is developed. Signals are separated into wanted sounds of specific interest and wind noise based on the statistical feature of wind noise. The new technique is based on the Singular Spectrum Analysis methodwhich has recently seen many successful paradigms in the separation of biomedical signals, e.g., separating heart soundfrom lung noise. It has also been successfully implemented to de-noise signals in various applications.The thesis set out with particular emphasison investigating the factor that determines and improves the separability towards obtaining satisfactory results in terms of separating wind noise components out from noisy acoustic signals. A systematicapproach has been established and developed within the framework of singular spectral separation of acoustic signals contaminated by wind noise. This approach, which utilisesa conceptual framework, has, in its final form, three key objectives; grouping, reconstruction and separability. This approach is offered through introducing new mathematical models particularly for window length optimisation along with new descriptive figures.The research question has therefore been addressed considering developing algorithms according to updated requirements from method justification to verification and validation of the developed system. This thesis follows suitable testing criteria by conducting several experiments and a case-study design, with in-depth analysis of the results using visual tools of the method and related techniques.For system verification, an empirical study using testing signals thatintroduces a large number of experiments has been conducted. Empirical study with real-world sounds has been introduced next in system validation phase after rigorously selecting and preparing the dataset whichis drawn from two main sources: freefield1010 dataset, internet-based Freesound recordings. Results show that microphone wind noise is separable in the singular spectrum domain after validating and critically evaluating the developed system objectively. The findings indicate the effectiveness of the developed grouping and reconstruction techniques with significant improvement in the separability evidenced by w-correlation matrix.The developed method might be generalised to other outdoor sound acquisition applications

A neural network model for speech intelligibility quantification

A neural network based model is developed to quantify speech intelligibility by blind-estimating speech transmission index, an objective rating index for speech intelligibility of transmission channels, from transmitted speech signals without resort to knowledge of original speech signals. It consists of a Hilbert transform processor for speech envelope detection, a Welch average periodogram algorithm for envelope spectrum estimation, a principal components analysis (PCA) network for speech feature extraction and a multi-layer back-propagation network for non-linear mapping and case generalisation. The developed model circumvents the use of artificial test signals by exploiting naturally occurring speech signals as probe stimuli, reduces measurement channels from two to one and hence facilitates in situ assessment of speech intelligibility. From a cognitive science viewpoint, the proposed method might be viewed as a successful paradigm of mimicking human perception of speech intelligibility using a hybrid model built around artificial neural networks