The purpose of the study was to compare auditory judgments of sound clarity of music examples recorded in a concert hall with predictions of clarity made from the impulse response signal recorded in the same hall. Auditory judgments were made with the use of two methods: by rating sound clarity on a numerical scale with two endpoints, and by absolute magnitude estimation. Results obtained by both methods were then compared against the values of clarity indices, C80 and C50, determined from the impulse response of the concert hall, measured in places in which the microphone was located during recording of music examples. Results show that auditory judgments of sound clarity and predictions made from the C80 index yield a similar rank order of data, but the relation between the C80 scale and perceived sound clarity is nonlinear. The data also show that the values of C80 and C50 indices are in very close agreement.

Psychoacoustics is traditionally based on a world model that assumes a physical world existing independently of any observer - the so-called objective world. Being exposed to this world, an observer is impinged upon by a variety of stimuli reaching his/her sensory organs. These stimuli, if physiologically adequate, may cause biological transduction and signal processing in the sensory organs and its afferent pathways in such a way that finally a specific excitation of the cortex takes place, which results in sen-sations to appear in the observer’s perceptual world. The sensations are understood as being subjective, since they require an observer to exist. This world model - also known as (objectivistic) realism - reaches its limits when it comes to explaining more complex phenomena of perception. Thereupon, in this paper, an alternative world model is emphasized and applied to psychoacoustics, namely the perceptionist’s model. Like realism, perceptionism has a long tradition in epistemology. It appears to be suitable to improve our understanding of perceptual organization.

During operation, construction machines generate high noise levels which can adversely affect the health and the job performance of operators. The noise control techniques currently applied to reduce the noise transmitted into the operator cab are all based on the decrease of the sound pressure level. Merely reducing this noise parameter may be suitable for the compliance with the legislative requirements but, unfortunately, it is not sufficient to improve the subjective human response to noise. The absolute necessity to guarantee comfortable and safe conditions for workers, requires a change of perspective and the identification of different noise control criteria able to combine the reduction of noise levels with that of psychophysical descriptors representing those noise attributes related to the subjective acoustical discomfort. This paper presents the results of a study concerning the “customization” of a methodology based on Sound Quality for the noise control of construction machines. The purpose is to define new hearing-related criteria for the noise control able to guarantee not only reduced noise levels at the operator position but also a reduced annoyance perception.

The purpose of this study was to develop a sound quality model for real time active sound quality control systems. The model is based on an optimal analytic wavelet transform (OAWT) used along with a back propagation neural network (BPNN) in which the initial weights and thresholds are determined by particle swarm optimisation (PSO). In the model the input signal is decomposed into 24 critical bands to extract a feature matrix, based on energy, mean, and standard deviation indices of the sub signal scalogram obtained by OAWT. The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. The results of the study show that the present model is in good agreement with psychoacoustic models of sound quality metrics and enables evaluation of the quality of sound at a lower computational cost than the existing models.

In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.

This paper reviews parametric audio coders and discusses novel technologies introduced in a low-complexity, low-power consumption audio decoder and music synthesizer platform developed by the authors. The decoder uses parametric coding scheme based on the MPEG-4 Parametric Audio standard. In order to keep the complexity low, most of the processing is performed in the parametric domain. This parametric processing includes pitch and tempo shifting, volume adjustment, selection of psychoacoustically relevant components for synthesis and stereo image creation. The decoder allows for good quality 44.1 kHz stereo audio streaming at 24 kbps. The synthesizer matches the audio quality of industry-standard sample-based synthesizers while using a twenty times smaller memory footprint soundbank. The presented decoder/synthesizer is designed for low-power mobile platforms and supports music streaming, ringtone synthesis, gaming and remixing applications.

The multi-stimulus test with hidden reference and anchors (MUSHRA) is commonly used for subjective quality assessment of audio systems. Despite its wide acceptance in scientific and industrial sectors, the method is not free from bias. One possible source of bias in the MUSHRA method may be attributed to a graphical design of its user interface. This paper examines the hypothesis that replacement of the standard multi-slider layout with a single-slider version could reduce a stimulus spacing bias observed in the MUSHRA test. Contrary to the expectation, the aforementioned modification did not reduce the bias. This outcome formally supports the validity of using multiple sliders in the MUSHRA graphical interface.