Patent Description:
The present disclosure generally relates to streaming of spatial audio, and specifically, to streaming of ambisonic spatial audio.

Streaming of spatial audio over networks requires efficient encoding techniques to compress raw audio content without compromising users' quality of experience (QoE). However, objective quality metrics to measure users' perceived quality and spatial localization accuracy are not currently available.

<NPL>, describes the coding of ambisonic signals with Spatially Squeezed Surround Audio Coding (S<NUM>AC). It further describes an evaluation of this coding against an original signal.

<NPL>, describes the use of NSIM to compare spectrograms for speech audio.

<NPL>, presents subjective tests evaluating the effect of compression of ambisonic signals on localization accuracy.

In one embodiment, a computing device includes a processor and a memory, where the processor is configured to generate spectrograms, for example, using short-time Fourier transform, for a plurality of channels of reference and test ambisonic signals. Ambisonics is a full-sphere surround sound format which covers sound sources above and below the listener in addition to the horizontal plane. In some implementations, the comparing may be based on phaseograms of the reference and test ambisonic signals.

Example implementations will become more fully understood from the detailed description given herein below and the accompanying drawings, wherein like elements are represented by like reference numerals, which are given by way of illustration only and thus are not limiting of the example implementations and wherein:.

It should be noted that these Figures are intended to illustrate the general characteristics of methods, structure, or materials utilized in certain example implementations and to supplement the written description provided below. These drawings are not, however, to scale and may not precisely reflect the precise structural or performance characteristics of any given implementation, and should not be interpreted as defining or limiting the range of values or properties encompassed by example implementation.

Perceptual Evaluation of Speech Quality (PESQ) and Perceptual Objective Listening Quality Assessment (POLQA) are full-reference measures, described in International Telecommunication Union (ITU) standards, to predict speech quality by comparing a reference signal to a received (or degraded) signal. Neurogram similarity index measure (NSIM) is a simplified version of structural similarity index measure (SSIM) for speech signal comparison with factors (e.g., luminance, structure, etc.) that give a weighted adjustment to the similarity measure that looks at the intensity (luminance), and cross-correlation (structure) between a given pixel and those that surround it versus the reference image. NSIM between two spectrograms, e.g., a reference spectrogram and a degraded spectrogram may be defined with a weighted function of intensity, contrast, and structure. In some implementations, for the purposes of neurogram comparisons for speech intelligibility estimation, the optimal window size may be a <NUM> × <NUM> pixel square covering three frequency bands and a <NUM>-ms time window.

Virtual Speech Quality Objective Listener (ViSQOL) is a signal-based, full-reference, intrusive metric that models human speech quality perception using a spectro-temporal measure of similarity between a reference and a test signal. ViSQOL also works with Voice over Internet Protocol (VoIP) transmissions (e.g., streaming audio), which may encounter quality issues due to the nature of VoIP. ViSQOL provides a useful alternative to other metrics, for example, POLQA, in predicting speech quality in VoIP transmissions or streaming audio.

ViSQOLAudio (V) is a full reference objective metric for measuring audio quality. It is based on using NSIM, a similarity measure that compares the similarity of signals by aligning and evaluating the similarity across time and frequency bands using a spectrogram-based comparison. ViSQOLAudio calculates magnitudes of the reference and test spectrograms using a <NUM>-band Gammatone filter bank (e.g., <NUM> - <NUM>) to compare their similarity. ViSQOLAudio may also pre-process the test signal with time alignment and perform level adjustments to match timing and power characteristics of the reference signal. After pre-preprocessing, the signals may be compared with the NSIM similarity metric. ViSQOL is a model of human sensitivity to degradations in speech quality. It compares a reference signal with a degraded signal. The output is a prediction of speech quality perceived by an average individual. Moreover, ViSQOL and ViSQOL audio contain subsystems that map raw NSIM similarity score (e.g., <NUM>-<NUM> scale) to a human perceptual scale mean opinion score (MOS).

The delivery of spatial audio for streaming services over limited bandwidth networks using higher order ambisonics (HOA) has driven development of various compression (e.g., encoding) techniques. This requires quality assessment methodologies to measure the perceptual quality of experience (QoE) for spatial audio using compressed ambisonics. However, unlike existing metrics for speech or regular audio quality assessment, an assessment of QoE of spatial audio must take into account not only the effects of audio fidelity degradations but also whether compression has altered the perceived localization of sound source origins.

The present disclosure provides an objective audio quality metric that assesses Listening Quality (LQ) and/or Localization Accuracy (LA) of compressed B-format ambisonic signals. For example, in one implementation, the present disclosure describes an objective metric, referred to as AMBIQUAL that predicts users' quality of experience (QoE) by estimating Listening Quality and/or Localization Accuracy of an audio signal. The objective metric may be determined (e.g., computed) using ambisonics, which can simulate placement of auditory cues in a virtual 3D space to allow a person's ability to determine the virtual origin of a detected sound. The present disclosure proposes a mechanism that eliminates the need for performing large scale listening tests that are costly and time-consuming. In some implementations, the proposed mechanism describes an objective audio quality metric that assesses LQ and/or LA of compressed B-format ambisonic signals without involving human listeners. The objective audio quality metric may be used in the development of audio processing methods, for example, for applications such as web browsers, virtual reality (VR)/augmented reality (AR), streaming video services and/or production quality control of spatial media. In some implementations, the proposed mechanism provides for improved encoding (and decoding) schemes to compress (decompress) the ambisonic signals. In some implementations, the objective audio quality metric may be used to determine whether the encoding mechanism is optimal based on the determined LA values.

Ambisonics is a full sphere audio surround technique that can be based upon the decomposition of a 3D sound field into a number of spherical harmonics signals. In contrast to channel-based methods with fixed speakers' layouts (e.g. stereo, surround <NUM>, surround <NUM>, etc.), ambisonics contain a speaker-independent representation of a 3D sound field known as B-format, which can be decoded to any speaker layout. The B-format may be especially useful in Augmented Reality (AR) and Virtual Reality (VR) applications as the format offers good audio signal manipulation possibilities (e.g., rendering audio in real-time according to head movements). The complete spatial audio information can be encoded into an ambisonics stream containing a number of spherical harmonics signals and scaled to any desired spatial order.

The AMBIQUAL model builds on an adaptation of the ViSQOLAudio algorithm. The AMBIQUAL model predicts perceived quality and spatial localization accuracy by computing signal similarity directly from the B-format ambisonic audio streams. As with ViSQOLAudio, the AMBIQUAL model derives a spectro-temporal measure of similarity between a reference and test audio signal. AMBIQUAL derives Listening Quality and Localization Accuracy metrics directly from the B-format ambisonic audio channels unlike other existing methods that evaluate binaurally rendered signals. The AMBIQUAL model predicts a composite QoE for the spatial audio signal that is not focused on a particular listening direction or a given head related transfer function (HRTF) that is used in rendering the binaural signal.

In some implementations, for example, a computing device may generate spectrograms for each channel of reference and test signals. The reference and test signals may be higher order ambisonics (e.g., third order) and the computing device may create (or generate) patches from each of the spectrograms. For example, the computing device may create one more patches for each channel of the reference and test signals. A patch may be a short duration of the entire signal, for example, <NUM> second in duration, and may a defined as a portion of the reference or test signal. Once the patches are created, the computing device may compare patches of the reference signal with corresponding patches (e.g., patches of a corresponding channel and with the closest match) of the test signal. The comparison may be performed using NSIM based on comparing spectrograms, phaseograms, or a combination thereof) to generate aggregate similarity scores. In one implementation, for example, the computing device may determine the Listening Quality based on an aggregate score associated with an omni-directional channel (e.g., channel <NUM>). In another implementation, for example, the computing device may determine Localization Accuracy based on a weighted sum of similarity scores between corresponding multi-directional channels (e.g., channels <NUM>-<NUM>).

<FIG> illustrates spherical harmonics <NUM> of a third order ambisonics stream. The spherical harmonics illustrated in <FIG> are sorted by increasing ambisonic channel number (ACN) and aligned for symmetry. The relevant spherical harmonics functions that may provide the direct-dependent amplitudes of each of the ambisonics signals are defined below in Table I.

For example, as illustrated in <FIG>, a first order ambisonics (1OA) audio <NUM> may be encoded into four spherical harmonics signals: an omni-directional component of order <NUM> (<NUM>) and three directional components of order <NUM> (<NUM>) - X (forward/backwards), Y (left/right), and Z (up/down). A second order ambisonics (2OA) audio <NUM> may be encoded into the omni-directional component of order <NUM> (<NUM>), the three directional components of order <NUM> (<NUM>), and five directional components of order <NUM> (<NUM>). A third order ambisonics (3OA) audio <NUM> may be encoded into the omni-directional component of order <NUM> (<NUM>), three directional components of order <NUM> (<NUM>), the five directional components of order <NUM> (<NUM>), and seven directional components of order <NUM> (<NUM>). An ambisonics stream (or signal) is said to be of order n when the ambisonics stream contains all the signals of orders <NUM> to n. Moreover, the corresponding directional spherical harmonics represent more complex polar patterns allowing more accurate source localization as ambisonics order increases. The use of higher order ambisonics (HOA) may improve Listening Quality and Localization accuracy (e.g., more directional spherical harmonics). However, higher amounts of processing resources may be needed to transform ambisonic multi-channel streams into a rendered soundscape. Therefore, streaming ambisonics (e.g., ambisonics data) over networks requires efficient encoding techniques to compress raw audio content in real time and without significantly compromising QoE.

In one implementation, omni-directional or multi-dimensional components of ambisonics may be referred to by ACNs, ambisonics of third order that may include <NUM> channels (of orders <NUM>-<NUM>), as shown below in Table I. In addition, Table I has formulas for ambisonics expressing amplitudes as a function of Azimuth (a) and Elevation (e), in one example implementation.

<FIG> illustrates a flowchart <NUM> for determining an objective quality metric for ambisonic spatial audio, according to least one example implementation.

In some implementations, a reference signal <NUM> and a test signal <NUM> may be inputs to a computing device (e.g., a computing device <NUM> of <FIG>) for executing the process of the flowchart <NUM>. The reference signal <NUM> and the test signal <NUM>, for example, may be B-format ambisonic signals, which, in one example, may be <NUM>-<NUM> seconds in duration. In one implementation, for example, the reference signal <NUM> and the test signal <NUM> may be 3OA signals. The test signal <NUM> may be extracted (e.g., decoded) from an encoded (or compressed) version of the reference signal <NUM> so that the QoE may be determined by taking into account signal degradations and any changes to the perceived localization of sound source origins due to the decoding/encoding process.

In one example implementation, the reference signal <NUM> (e.g., reference ambisonic audio sources) may be rendered to <NUM> fixed localizations that may be evenly distributed on a quarter of the sphere. The test signal <NUM> (e.g., test ambisonic audio signals) may be rendered at <NUM> fixed localizations that may be evenly distributed on the whole sphere (e.g., with <NUM> horizontal and vertical steps).

At block <NUM>, the computing device may create spectrograms (that may be referred to as reference spectrograms or reference phaseograms) of each channel of the reference signal <NUM>. For example, <NUM> spectrograms of the reference signal <NUM> may be created, one spectrogram of each channel of the reference signal <NUM>. At block <NUM>, the computing device may create spectrograms (that may be referred to as test spectrograms or test phaseograms) of each channel of the test signal <NUM>. For example, <NUM> spectrograms may be created, one spectrogram of each channel of the test signal <NUM>.

In some implementations, the spectrograms of the test signal <NUM> and the reference signal <NUM> may be created using short-time Fourier transform (STFT) of their respective ambisonic channels. For instance, a STFT with a <NUM>-point Hamming window (e.g., <NUM>% overlap) may be applied to the channels of the reference signal <NUM> and the test signal <NUM> to generate the spectrograms. In one implementation, for example, the generated spectrograms may be phaseograms (also referred to as phase spectrograms). In a phaseogram, phase values of STFT may be processed and presented graphically such that time-frequency distribution of the phase of a component may provide information about phase modulations around a reference point to determine reference phase and reference frequency for the component. For instance, the STFT may create a spectrogram of real and imaginary numbers for every time/frequency from which the phase of every frequency at any given time may be extracted. In one more implementation, the spectrograms may be generated based on intensities or a combination of phase angles and intensities.

For instance, a spectrogram, z, may be a matrix that is computed using a short-time Fourier transform of an input signal using a <NUM>-poing Hamming window (e.g., <NUM>% overlap). The matrix may contain real and imaginary components and a phaseogram is a corresponding phase angle matrix of the spectrogram that is computed from the spectrogram using the equation below, <MAT> where atan2 is a four-quadrant inverse tangent. For example, atan2(Y, X) may return values in the closed interval [-pi, pi] based on values of Y and X as shown in the graphic below:
<IMG>.

At block <NUM>, the computing device may segment the reference spectrograms generated at block <NUM> into patches (that may be referred to as reference patches). That is, one or more reference patches may be created for each channel of the reference signal <NUM> from the respective reference spectrograms. In some implementations, the computing device may create (or generate) one or more patches from each of the reference spectrograms. A reference patch may be generated from a portion of the reference signal <NUM>, for example, <NUM> seconds long and may be created using STFT. In one implementation, for example, a reference patch may be a <NUM> × <NUM> matrix (e.g., <NUM> frequency bands × <NUM> time frames). The references patches may be used for comparing with corresponding patches generated from the test signal <NUM> to compute similarity scores to determine Listening Quality and/or Localization Accuracy.

At block <NUM>, the computing device may segment the test spectrograms generated at block <NUM> into patches (may be referred to as test patches). That is, one or more test patches may be created for each channel of the test signal <NUM> from the respective test spectrograms. In some implementations, the computing device may create (or generate) one or more patches from each of the test spectrograms. Similar to the reference patches, a test patch may be, for example, <NUM> seconds long and may be created using STFT. In one implementation, for example, a test patch may be a <NUM> × <NUM> matrix (e.g., <NUM> frequency bands × <NUM> time frames). The test patches may be used for comparing with the corresponding reference patches to compute similarity scores to determine Listening Quality and/or Localization Accuracy.

In some implementations, at block <NUM>, the test patches and the reference patches may be aligned with each other. The alignment (e.g., time alignment) may be performed, prior to comparing of the reference and test patches, to ensure that a reference patch is being compared with a corresponding test patch that is most similar. In other words, the alignment may be performed to time-align the patches prior to the comparison.

At block <NUM>, the computing device may compare reference patches with test patches. In some implementations, the comparing may be performed using NSIM which may compare patches across all frequency bands and compute aggregate similarity scores at block <NUM>. As described above, NSIM is a similarity measure for comparing spectrograms of reference patches and test patches to compute similarity scores. In one implementation, for example, the comparison may be based on phase angles and NSIM may compare the phases in each of the points in the <NUM> × <NUM> matrices (associated with the reference and test patches) and compute the average value to generate the NSIM values.

In some implementations, at <NUM>, the Listening Quality may be determined based on an aggregate score of channel <NUM> based on the comparing of one or more patches of channel <NUM> (e.g., k = <NUM>). That is, the Listening Quality may be determined based on aggregate similarity scores of channel <NUM>, the omni-directional channel <NUM>. The omni-directional channel <NUM> is considered to contain a composite of directional channels and the content of the omni-directional channel <NUM> may be considered to be a good (e.g., representative) indicator of the Listening Quality (e.g., due to encoding artefacts and without localization differences). In one implementation, for example, the Listening Quality (LQ) may be computed by applying a ViSQOLAudio algorithm to the phaseograms of channel <NUM> (e.g., k = <NUM>) of the reference signal <NUM> (r) and the test signal <NUM> (t) as shown in the following equation, <MAT> where LQ is the listening quality, V is ViSQOLAudio algorithm, r<NUM> is the reference phaseograms of channel <NUM>, and t<NUM> is the test phaseograms of channel <NUM>.

For example, the LQ may be computed using ViSQOLAudio model (described above) that measures similarity scores using NSIM for patches of channel <NUM>.

In some implementations, the LQ scores may have values between <NUM> and <NUM>, with a value of <NUM> being a perfect match. That is, a test patch matches perfectly with a corresponding reference patch.

At <NUM>, the Localization Accuracy (LA) is determined based on aggregate similarity scores of channels <NUM> to K (e.g., channels <NUM> to <NUM> for 3OA). That is, the similarity scores of channels <NUM>-<NUM> are computed and aggregated to determine the aggregate similarity score. The LA is determined as a weighted sum of similarity between the reference and test channels. That is, different weights may be assigned to the various directional components of channels <NUM>-<NUM>.

For instance, the channels (e.g., <NUM>-<NUM>) may be grouped into vertical-only channels and mixed direction channels. For 3OA, channels <NUM>, <NUM>, and <NUM> are vertical-only channels. For higher order ambisonics, the vertical-only channels may be determined as shown below: <MAT>.

The LA may be computed as a weighted sum of similarity between reference patch, r, and test patch, t, as shown in the following equation, <MAT> where LA is the listening quality, V is ViSQOLAudio algorithm, and alpha (α) is a parameter that controls trade-off between vertical and horizontal components. In the above equation, in the first summation, rk is the reference phaseogram of vertical component channel k, and tk is the test phaseogram of vertical component channel k. In the above equation, in the second summation, rk is the reference phaseogram of mixed component channel k, and tk is the test phaseogram of mixed component channel k.

For example, the LA may be computed using the ViSQOLAudio model (described above) that measures NSIM similarity scores, for example, for channels <NUM>-<NUM> for third order ambisonics. The value of alpha (α) may control a trade-off between the importance of vertical and horizontal components (e.g., control bias). That is, the higher the value of α, the more emphasis may be given to vertical channel similarity (vs horizontal channel similarity). Thus, as described above, the Listening Quality and/or the Localization Accuracy of ambisonic spatial audio may be determined by computing aggregate similarity scores of channel <NUM> and channels <NUM>-<NUM>, respectively, of the ambisonic spatial audio. In some other implementations, the value of alpha may be channel dependent. In other words, different channels may have different alpha values to control the trade-off between the importance of vertical and horizontal components on a per-channel basis and/or the value of alpha may change depending on the ambisonic order.

<FIG> illustrates a flowchart <NUM> of a method of determining quality of experience (QoE) of ambisonics spatial audio according to least one example implementation.

At block <NUM>, a computing device compares a patch associated with multi-directional channels of the reference ambisonic signal with a corresponding patch of the corresponding multi-directional channels of a test ambisonic signal. The comparison is performed for each of a plurality of channels of reference and test ambisonic signals. The test ambisonic signal is generated by decoding an encoded version of the reference ambisonic signal and the comparison may be based on phaseograms of the reference ambisonic signal and the test ambisonic signal. For example, the computing device may compare at least one patch associated with each channel of the reference signal <NUM> with at least the corresponding patch of the test signal <NUM>. For instance, the computing device may compare patch <NUM> of channel <NUM> of the reference signal <NUM> with patch <NUM> of channel <NUM> of the test signal <NUM>, and compare patch <NUM> of channel <NUM> of the reference signal <NUM> with patch <NUM> of channel <NUM> of the test signal <NUM>, and so on.

At block <NUM>, the computing device determines a localization accuracy of the test ambisonic signal based on the comparison. The comparison is performed using NSIM, as described above in reference to <FIG>, to generate similarity scores. In one implementation for example, the computing device may determine the listening quality may be based on an aggregate score that is based on comparing of the omni-directional components (or channels) of the reference signal and the test signal. The computing device determines the localization accuracy based on a weighted sum of similarity scores between corresponding multi-directional channels (e.g., channels <NUM>-<NUM>) of the test and reference signals. Thus, the localization accuracy of an ambisonic spatial audio is determined, and in one or more implementation, the listening quality is also determined.

<FIG> illustrates a flowchart <NUM> of a method of determining quality of experience (QoE) of ambisonics spatial audio, according to least another example implementation.

At block <NUM>, a computing device may generate spectrograms of the plurality of channels of the reference ambisonic signal and the test ambisonic signal. In some implementations, the computing device may generate spectrograms of the plurality of channels of the reference ambisonic signal <NUM> and test ambisonic signal <NUM>, as described above in reference to <FIG>. The spectrograms may be created using STFT.

At block <NUM>, the computing device may align, prior to comparing, the patch associated with the channel of the reference ambisonic signal with the corresponding patch of the corresponding channel of the test ambisonic signal. In some implementations, the computing device may align corresponding patches with each other prior to comparison to provide for the patches with the best match to be compared with each other.

At block <NUM>, the operations are similar to operations at block <NUM> of <FIG>.

Thus, the listening quality and/or localization accuracy of an ambisonic spatial audio are determined.

<FIG> shows an example of a computer device <NUM> and a mobile computer device <NUM>, which may be used with the techniques described here. Computing device <NUM> is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Computing device <NUM> is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, and other similar computing devices.

The computer program product can be tangibly embodied in an information carrier.

In addition, an external interface <NUM> may be provide in communication with processor <NUM>, to enable near area communication of device <NUM> with other devices.

In addition, short-range communication may occur, such as using a Bluetooth, Wi-Fi, or other such transceiver (not shown).

Various implementations of the systems and techniques described here can be realized as and/or generally be referred to herein as a circuit, a module, a block, or a system that can combine software and hardware aspects. For example, a module may include the functions/acts/computer program instructions executing on a processor (e.g., a processor formed on a silicon substrate, a GaAs substrate, and the like) or some other programmable data processing apparatus.

Some of the above example embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of operations may be rearranged. The processes may be terminated when their operations are completed, but may also have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, subprograms, etc..

Methods discussed above, some of which are illustrated by the flow charts, may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine or computer readable medium such as a storage medium.

Specific structural and functional details disclosed herein are merely representative for purposes of describing example embodiments. Example embodiments, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.

For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. As used herein, the term and/or includes any and all combinations of one or more of the associated listed items.

It will be understood that when an element is referred to as being connected or coupled to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being directly connected or directly coupled to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., between versus directly between, adjacent versus directly adjacent, etc.).

As used herein, the singular forms a, an, and the are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms comprises, comprising, includes and/or including, when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.

For example, two figures shown in succession may in fact be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

Portions of the above example implementations and corresponding detailed description are presented in terms of software, or algorithms and symbolic representations of operation on data bits within a computer memory. These descriptions and representations are the ones by which those of ordinary skill in the art effectively convey the substance of their work to others of ordinary skill in the art. An algorithm, as the term is used here, and as it is used generally, is conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of optical, electrical, or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated.

In the above illustrative implementations, reference to acts and symbolic representations of operations (e.g., in the form of flowcharts) that may be implemented as program modules or functional processes include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types and may be described and/or implemented using existing hardware at existing structural elements. Such existing hardware may include one or more Central Processing Units (CPUs), digital signal processors (DSPs), application-specific-integrated-circuits, field programmable gate arrays (FPGAs) computers or the like.

Unless specifically stated otherwise, or as is apparent from the discussion, terms such as processing or computing or calculating or determining of displaying or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical, electronic quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Note also that the software implemented aspects of the example implementations are typically encoded on some form of non-transitory program storage medium or implemented over some type of transmission medium. The program storage medium may be magnetic (e.g., a floppy disk or a hard drive) or optical (e.g., a compact disk read only memory, or CD ROM), and may be read only or random access. Similarly, the transmission medium may be twisted wire pairs, coaxial cable, optical fiber, or some other suitable transmission medium known to the art. The example implementations not limited by these aspects of any given implementation.

Claim 1:
A computer-implemented method of determining quality of experience (QoE) of ambisonic spatial audio signals, comprising:
comparing, based on a neurogram similarity index measure, NSIM, a patch associated with each of a plurality of multi-directional channels of a reference ambisonic signal with a corresponding patch of a corresponding multi-directional channel of a test ambisonic signal, the test ambisonic signal generated by decoding an encoded version of the reference ambisonic signal; and
determining a localization accuracy of the test ambisonic signal based on the comparison by determining an aggregated NSIM that is based on a weighted sum of NSIMs between corresponding multi-directional channels of the test ambisonic signal and the reference ambisonic signal, wherein, in the weighted sum, weights are assigned to vertical and horizontal components of the multi-directional channels in order to change the emphasis between horizontal and vertical channel similarity.