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The automatic localization of audio sources distributed symmetrically with respect to coronal or transverse planes using binaural signals still poses a challenging task, due to the front–back and up–down confusion effects. This paper demonstrates that the convolutional neural network (CNN) can be used to automatically localize music ensembles panned to the front, back, up, or down positions. The network was developed using the repository of the binaural excerpts obtained by the convolution of multi-track music recordings with the selected sets of head-related transfer functions (HRTFs). They were generated in such a way that a music ensemble (of circular shape in terms of its boundaries) was positioned in one of the following four locations with respect to the listener: front, back, up, and down. According to the obtained results, CNN identified the location of the ensembles with the average accuracy levels of 90.7% and 71.4% when tested under the HRTF-dependent and HRTF-independent conditions, respectively. For HRTF-dependent tests, the accuracy decreased monotonically with the increase in the ensemble size. A modified image occlusion sensitivity technique revealed selected frequency bands as being particularly important in terms of the localization process. These frequency bands are largely in accordance with the psychoacoustical literature. [ABSTRACT FROM AUTHOR]

Featured Application: The perceptual attributes elicited in this study are expected to serve as a theoretical basis for virtual acoustic rendering in 6-degrees-of-freedom extended reality applications. This paper presents a subjective study conducted on the perception of auditory attributes depending on listener position and head orientation in an enclosed space. Two elicitation experiments were carried out using the repertory grid technique—in-situ and laboratory experiments—which aimed to identify perceptual attributes among 10 different combinations of the listener's positions and head orientations in a concert hall. It was found that, between the in-situ and laboratory experiments, the listening positions and head orientations were clustered identically. Ten salient perceptual attributes were identified from the data obtained from the laboratory experiment. Whilst these included conventional attributes such as ASW (apparent source width) and LEV (listener envelopment), new attributes such as PRL (perceived reverb loudness), ARW (apparent reverb width) and Reverb Direction were identified, and they are hypothesised to be sub-attributes of LEV (listener envelopment). Timbral characteristics such as Reverb Brightness and Echo Brightness were also identified as salient attributes, which are considered to potentially contribute to the overall perceived clarity. [ABSTRACT FROM AUTHOR]

Featured Application: In Auditory Augmented Reality (AAR), the real room is enriched by virtual audio objects. Position-dynamic binaural synthesis is used to auralize the audio objects for moving listeners and to create a plausible experience of the mixed reality scenario. For a spatial audio reproduction in the context of augmented reality, a position-dynamic binaural synthesis system can be used to synthesize the ear signals for a moving listener. The goal is the fusion of the auditory perception of the virtual audio objects with the real listening environment. Such a system has several components, each of which help to enable a plausible auditory simulation. For each possible position of the listener in the room, a set of binaural room impulse responses (BRIRs) congruent with the expected auditory environment is required to avoid room divergence effects. Adequate and efficient approaches are methods to synthesize new BRIRs using very few measurements of the listening room. The required spatial resolution of the BRIR positions can be estimated by spatial auditory perception thresholds. Retrieving and processing the tracking data of the listener's head-pose and position as well as convolving BRIRs with an audio signal needs to be done in real-time. This contribution presents work done by the authors including several technical components of such a system in detail. It shows how the single components are affected by psychoacoustics. Furthermore, the paper also discusses the perceptive effect by means of listening tests demonstrating the appropriateness of the approaches. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to compare the performance of human listeners against the selected machine learning algorithms in the task of the classification of spatial audio scenes in binaural recordings of music under practical conditions. The three scenes were subject to classification: (1) music ensemble (a group of musical sources) located in the front, (2) music ensemble located at the back, and (3) music ensemble distributed around a listener. In the listening test, undertaken remotely over the Internet, human listeners reached the classification accuracy of 42.5%. For the listeners who passed the post-screening test, the accuracy was greater, approaching 60%. The above classification task was also undertaken automatically using four machine learning algorithms: convolutional neural network, support vector machines, extreme gradient boosting framework, and logistic regression. The machine learning algorithms substantially outperformed human listeners, with the classification accuracy reaching 84%, when tested under the binaural-room-impulse-response (BRIR) matched conditions. However, when the algorithms were tested under the BRIR mismatched scenario, the accuracy obtained by the algorithms was comparable to that exhibited by the listeners who passed the post-screening test, implying that the machine learning algorithms capability to perform in unknown electro-acoustic conditions needs to be further improved. [ABSTRACT FROM AUTHOR]

This study examined whether the spatial unmasking effect operates on speech reception thresholds (SRTs) in the median plane. SRTs were measured using an adaptive staircase procedure, with target speech sentences and speech-shaped noise maskers presented via loudspeakers at −30°, 0°, 30°, 60° and 90°. Results indicated a significant median plane spatial unmasking effect, with the largest SRT gain obtained for the −30° elevation of the masker. Head-related transfer function analysis suggests that the result is associated with the energy weighting of the ear-input signal of the masker at upper-mid frequencies relative to the maskee. [ABSTRACT FROM AUTHOR]

Direct sound that is captured by the upper layer of a three-dimensional (3D) microphone array is typically regarded as vertical interchannel crosstalk (VIC), since it tends to produce an undesired effect of the sound source image being elevated from the ear-level loudspeaker layer position (0°) in reproduction. The present study examined the effectiveness of band-limited VIC attenuation methods on preventing the vertical image shift problem. In a subjective experiment, five natural sound sources were presented as vertically-oriented phantom images while using two stereophonic loudspeaker pairs elevated at 0° and 30° in front of the listener. The upper layer signal (i.e., VIC) was attenuated in various octave-band-dependent conditions that were based on vertical localisation thresholds obtained from previous studies. The results showed that it was possible to achieve the goal of panning the phantom image at the same height as the image produced by the main loudspeaker layer by attenuating only a single octave band with the centre frequency of 4 kHz or 8 kHz or multiple bands at 1 kHz and above. This has a useful practical implication in 3D sound recording and mixing where a vertically oriented phantom image is rendered. [ABSTRACT FROM AUTHOR]

The aim of the study was to develop a method for automatic classification of the three spatial audio scenes, differing in horizontal distribution of foreground and background audio content around a listener in binaurally rendered recordings of music. For the purpose of the study, audio recordings were synthesized using thirteen sets of binaural-room-impulse-responses (BRIRs), representing room acoustics of both semi-anechoic and reverberant venues. Head movements were not considered in the study. The proposed method was assumption-free with regards to the number and characteristics of the audio sources. A least absolute shrinkage and selection operator was employed as a classifier. According to the results, it is possible to automatically identify the spatial scenes using a combination of binaural and spectro-temporal features. The method exhibits a satisfactory classification accuracy when it is trained and then tested on different stimuli but synthesized using the same BRIRs (accuracy ranging from 74% to 98%), even in highly reverberant conditions. However, the generalizability of the method needs to be further improved. This study demonstrates that in addition to the binaural cues, the Mel-frequency cepstral coefficients constitute an important carrier of spatial information, imperative for the classification of spatial audio scenes. [ABSTRACT FROM AUTHOR]

In subjective listening tests, natural sound sources were presented to subjects as vertically-oriented phantom images from two layers of loudspeakers, 'height' and 'main'. Subjects were required to reduce the amplitude of the height layer until the position of the resultant sound source matched that of the same source presented from the main layer only (the localisation threshold). Delays of 0, 1 and 10 ms were applied to the height layer with respect to the main, with vertical stereophonic and quadraphonic conditions being tested. The results of the study showed that the localisation thresholds obtained were not significantly affected by sound source or presentation method. Instead, the only variable whose effect was significant was interchannel time difference (ICTD). For ICTD of 0 ms, the median threshold was -9.5 dB, which was significantly lower than the -7 dB found for both 1 and 10 ms. The results of the study have implications both for the recording of sound sources for three-dimensional (3D) audio reproduction formats and also for the rendering of 3D images. [ABSTRACT FROM AUTHOR]