ODAQ: Open Dataset of Audio Quality

Research into the prediction and analysis of perceived audio quality is hampered by the scarcity of openly available datasets of audio signals accompanied by corresponding subjective quality scores. To address this problem, we present the Open Dataset of Audio Quality (ODAQ), a new dataset containing the results of a MUSHRA listening test conducted with expert listeners from 2 international laboratories. ODAQ contains 240 audio samples and corresponding quality scores. Each audio sample is rated by 26 listeners. The audio samples are stereo audio signals sampled at 44.1 or 48 kHz and are processed by a total of 6 method classes, each operating at different quality levels. The processing method classes are designed to generate quality degradations possibly encountered during audio coding and source separation, and the quality levels for each method class span the entire quality range. The diversity of the processing methods, the large span of quality levels, the high sampling frequency, and the pool of international listeners make ODAQ particularly suited for further research into subjective and objective audio quality. The dataset is released with permissive licenses, and the software used to conduct the listening test is also made publicly available.

Predicting Preferred Dialogue-to-Background Loudness Difference in Dialogue-Separated Audio

Dialogue Enhancement (DE) enables the rebalancing of dialogue and background sounds to fit personal preferences and needs in the context of broadcast audio. When individual audio stems are unavailable from production, Dialogue Separation (DS) can be applied to the final audio mixture to obtain estimates of these stems. This work focuses on Preferred Loudness Differences (PLDs) between dialogue and background sounds. While previous studies determined the PLD through a listening test employing original stems from production, stems estimated by DS are used in the present study. In addition, a larger variety of signal classes is considered. PLDs vary substantially across individuals (average interquartile range: 5.7 LU). Despite this variability, PLDs are found to be highly dependent on the signal type under consideration, and it is shown that median PLDs can be predicted using objective intelligibility metrics. Two existing baseline prediction methods - intended for use with original stems - displayed a Mean Absolute Error (MAE) of 7.5 LU and 5 LU, respectively. A modified baseline (MAE: 3.2 LU) and an alternative approach (MAE: 2.5 LU) are proposed. Results support the viability of processing final broadcast mixtures with DS and offering an alternative remixing that accounts for median PLDs.

Better Together: Dialogue Separation and Voice Activity Detection for Audio Personalization in TV

In TV services, dialogue level personalization is key to meeting user preferences and needs. When dialogue and background sounds are not separately available from the production stage, Dialogue Separation (DS) can estimate them to enable personalization. DS was shown to provide clear benefits for the end user. Still, the estimated signals are not perfect, and some leakage can be introduced. This is undesired, especially during passages without dialogue. We propose to combine DS and Voice Activity Detection (VAD), both recently proposed for TV audio. When their combination suggests dialogue inactivity, background components leaking in the dialogue estimate are reassigned to the background estimate. A clear improvement of the audio quality is shown for dialogue-free signals, without performance drops when dialogue is active. A post-processed VAD estimate with improved detection accuracy is also generated. It is concluded that DS and VAD can improve each other and are better used together.

Dialogue Enhancement and Listening Effort in Broadcast Audio: A Multimodal Evaluation

Dialogue enhancement (DE) plays a vital role in broadcasting, enabling the personalization of the relative level between foreground speech and background music and effects. DE has been shown to improve the quality of experience, intelligibility, and self-reported listening effort (LE). A physiological indicator of LE known from audiology studies is pupil size. The relation between pupil size and LE is typically studied using artificial sentences and background noises not encountered in broadcast content. This work evaluates the effect of DE on LE in a multimodal manner that includes pupil size (tracked by a VR headset) and real-world audio excerpts from TV. Under ideal listening conditions, 28 normal-hearing participants listened to 30 audio excerpts presented in random order and processed by conditions varying the relative level between foreground and background audio. One of these conditions employed a recently proposed source separation system to attenuate the background given the original mixture as the sole input. After listening to each excerpt, subjects were asked to repeat the heard sentence and self-report the LE. Mean pupil dilation and peak pupil dilation were analyzed and compared with the self-report and the word recall rate. The multimodal evaluation shows a consistent trend of decreasing LE along with decreasing background level. DE, also when enabled by source separation, significantly reduces the pupil size as well as the self-reported LE. This highlights the benefit of personalization functionalities at the user's end.

Geometrically-Motivated Primary-Ambient Decomposition With Center-Channel Extraction

A geometrically-motivated method for primary-ambient decomposition is proposed and evaluated in an up-mixing application. The method consists of two steps, accommodating a particularly intuitive explanation. The first step consists of signal-adaptive rotations applied on the input stereo scene, which translate the primary sound sources into the center of the rotated scene. The second step applies a center-channel extraction method, based on a simple signal model and optimal in the mean-squared-error sense. The performance is evaluated by using the estimated ambient component to enable surround sound starting from real-world stereo signals. The participants in the reported listening test are asked to adjust the audio scene envelopment and find the audio settings that pleases them the most. The possibility for up-mixing enabled by the proposed method is used extensively, and the user satisfaction is significantly increased compared to the original stereo mix.

Sampling Frequency Independent Dialogue Separation

In some DNNs for audio source separation, the relevant model parameters are independent of the sampling frequency of the audio used for training. Considering the application of dialogue separation, this is shown for two DNN architectures: a U-Net and a fully-convolutional model. The models are trained with audio sampled at 8 kHz. The learned parameters are transferred to models for processing audio at 48 kHz. The separated audio sources are compared with the ones produced by the same model architectures trained with 48 kHz versions of the same training data. A listening test and computational measures show that there is no significant perceptual difference between the models trained with 8 kHz or with 48 kHz. This transferability of the learned parameters allows for a faster and computationally less costly training. It also enables using training datasets available at a lower sampling frequency than the one needed by the application at hand, or using data collections with multiple sampling frequencies.

Dialog+ in Broadcasting: First Field Tests Using Deep-Learning-Based Dialogue Enhancement

Difficulties in following speech due to loud background sounds are common in broadcasting. Object-based audio, e.g., MPEG-H Audio solves this problem by providing a user-adjustable speech level. While object-based audio is gaining momentum, transitioning to it requires time and effort. Also, lots of content exists, produced and archived outside the object-based workflows. To address this, Fraunhofer IIS has developed a deep-learning solution called Dialog+, capable of enabling speech level personalization also for content with only the final audio tracks available. This paper reports on public field tests evaluating Dialog+, conducted together with Westdeutscher Rundfunk (WDR) and Bayerischer Rundfunk (BR), starting from September 2020. To our knowledge, these are the first large-scale tests of this kind. As part of one of these, a survey with more than 2,000 participants showed that 90% of the people above 60 years old have problems in understanding speech in TV "often" or "very often". Overall, 83% of the participants liked the possibility to switch to Dialog+, including those who do not normally struggle with speech intelligibility. Dialog+ introduces a clear benefit for the audience, filling the gap between object-based broadcasting and traditionally produced material.

Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence

Over the past few decades, computational methods have been developed to estimate perceptual audio quality. These methods, also referred to as objective quality measures, are usually developed and intended for a specific application domain. Because of their convenience, they are often used outside their original intended domain, even if it is unclear whether they provide reliable quality estimates in this case. This work studies the correlation of well-known state-of-the-art objective measures with human perceptual scores in two different domains: audio coding and source separation. The following objective measures are considered: fwSNRseg, dLLR, PESQ, PEAQ, POLQA, PEMO-Q, ViSQOLAudio, (SI-)BSSEval, PEASS, LKR-PI, 2f-model, and HAAQI. Additionally, a novel measure (SI-SA2f) is presented, based on the 2f-model and a BSSEval-based signal decomposition. We use perceptual scores from 7 listening tests about audio coding and 7 listening tests about source separation as ground-truth data for the correlation analysis. The results show that one method (2f-model) performs significantly better than the others on both domains and indicate that the dataset for training the method and a robust underlying auditory model are crucial factors towards a universal, domain-independent objective measure.

Controlling the Perceived Sound Quality for Dialogue Enhancement with Deep Learning

Speech enhancement attenuates interfering sounds in speech signals but may introduce artifacts that perceivably deteriorate the output signal. We propose a method for controlling the trade-off between the attenuation of the interfering background signal and the loss of sound quality. A deep neural network estimates the attenuation of the separated background signal such that the sound quality, quantified using the Artifact-related Perceptual Score, meets an adjustable target. Subjective evaluations indicate that consistent sound quality is obtained across various input signals. Our experiments show that the proposed method is able to control the trade-off with an accuracy that is adequate for real-world dialogue enhancement applications.

Controlling the Remixing of Separated Dialogue with a Non-Intrusive Quality Estimate

Remixing separated audio sources trades off interferer attenuation against the amount of audible deteriorations. This paper proposes a non-intrusive audio quality estimation method for controlling this trade-off in a signal-adaptive manner. The recently proposed 2f-model is adopted as the underlying quality measure, since it has been shown to correlate strongly with basic audio quality in source separation. An alternative operation mode of the measure is proposed, more appropriate when considering material with long inactive periods of the target source. The 2f-model requires the reference target source as an input, but this is not available in many applications. Deep neural networks (DNNs) are trained to estimate the 2f-model intrusively using the reference target (iDNN2f), non-intrusively using the input mix as reference (nDNN2f), and reference-free using only the separated output signal (rDNN2f). It is shown that iDNN2f achieves very strong correlation with the original measure on the test data (Pearson r=0.99), while performance decreases for nDNN2f (r>=0.91) and rDNN2f (r>=0.82). The non-intrusive estimate nDNN2f is mapped to select item-dependent remixing gains with the aim of maximizing the interferer attenuation under a constraint on the minimum quality of the remixed output (e.g., audible but not annoying deteriorations). A listening test shows that this is successfully achieved even with very different selected gains (up to 23 dB difference).