Deep learning, especially in the form of convolutional neural networks (CNNs), has triggered substantial improvements in computer vision and related fields in recent years. This progress is attributed to the shift from designing features and subsequent individual subsystems towards learning features and recognition systems end to end from nearly unprocessed data. For speaker clustering, however, it is still common to use handcrafted processing chains such as MFCC features and GMM-based models. In this paper, we use simple spectrograms as input to a CNN and study the optimal design of those networks for speaker identification and clustering. Furthermore, we elaborate on the question how to transfer a network, trained for speaker identification, to speaker clustering. We demonstrate our approach on the well known TIMIT dataset, achieving results comparable with the state of the art-without the need for handcrafted features.

Recent work has shown that convolutional neural networks (CNNs) trained in a supervised fashion for speaker identification are able to extract features from spectrograms which can be used for speaker clustering. These features are represented by the activations of a certain hidden layer and are called embeddings. However, previous approaches require plenty of additional speaker data to learn the embedding, and although the clustering results are then on par with more traditional approaches using MFCC features etc., room for improvements stems from the fact that these embeddings are trained with a surrogate task that is rather far away from segregating unknown voices-namely, identifying few specific speakers. We address both problems by training a CNN to extract embeddings that are similar for equal speakers (regardless of their specific identity) using weakly labeled data. We demonstrate our approach on the well-known TIMIT dataset that has often been used for speaker clustering experiments in the past. We exceed the clustering performance of all previous approaches, but require just 100 instead of 590 unrelated speakers to learn an embedding suited for clustering.

Background Slow-paced breathing training (6 breaths per minute [BPM]) improves physiological and psychological well-being by inducing relaxation characterized by increased heart rate variability (HRV). However, classic breathing training has a limited target group, and retention rates are very low. Although a gameful approach may help overcome these challenges, it is crucial to enable breathing training in a scalable context (eg, smartphone only) and ensure that they remain effective. However, despite the health benefits, no validated mobile gameful breathing training featuring a biofeedback component based on breathing seems to exist. Objective This study aims to describe the design choices and their implementation in a concrete mobile gameful breathing training app. Furthermore, it aims to deliver an initial validation of the efficacy of the resulting app. Methods Previous work was used to derive informed design choices, which, in turn, were applied to build the gameful breathing training app Breeze. In a pretest (n=3), design weaknesses in Breeze were identified, and Breeze was adjusted accordingly. The app was then evaluated in a pilot study (n=16). To ascertain that the effectiveness was maintained, recordings of breathing rates and HRV-derived measures (eg, root mean square of the successive differences [RMSSDs]) were collected. We compared 3 stages: baseline, standard breathing training deployed on a smartphone, and Breeze. Results Overall, 5 design choices were made: use of cool colors, natural settings, tightly incorporated game elements, game mechanics reflecting physiological measures, and a light narrative and progression model. Breeze was effective, as it resulted in a slow-paced breathing rate of 6 BPM, which, in turn, resulted in significantly increased HRV measures compared with baseline (P<.001 for RMSSD). In general, the app was perceived positively by the participants. However, some criticized the somewhat weaker clarity of the breathing instructions when compared with a standard breathing training app. Conclusions The implemented breathing training app Breeze maintained its efficacy despite the use of game elements. Moreover, the app was positively perceived by participants although there was room for improvement.

Slow-paced biofeedback-guided breathing training has been shown to improve cardiac functioning and psychological wellbeing. Current training options, however, attract only a fraction of individuals and are limited in their scalability as they require dedicated biofeedback hardware. In this work, we present Breeze, a mobile application that uses a smartphone's microphone to continuously detect breathing phases, which then trigger a gamified biofeedback-guided breathing training. Circa 2.76 million breathing sounds from 43 subjects and control sounds were collected and labeled to train and test our breathing detection algorithm. We model breathing as inhalation-pause-exhalation-pause sequences and implement a phase-detection system with an attention-based LSTM model in conjunction with a CNN-based breath extraction module. A biofeedback-guided breathing training with Breeze takes place in real-time and achieves 75.5% accuracy in breathing phases detection. Breeze was also evaluated in a pilot study with 16 new subjects, which demonstrated that the majority of subjects prefer Breeze over a validated active control condition in its usefulness, enjoyment, control, and usage intentions. Breeze is also effective for strengthening users' cardiac functioning by increasing high-frequency heart rate variability. The results of our study suggest that Breeze could potentially be utilized in clinical and self-care activities. Sailboat In h a le P a u se E xh a le P a u se … Nose Mouth R e p e a te d a co u st ic b re a th in g se q u e n ce Fig. 1. Overview of Breeze, a mobile gamified biofeedback breathing training.

Background Slow-paced breathing training can have positive effects on physiological and psychological well-being. Unfortunately, use statistics indicate that adherence to breathing training apps is low. Recent work suggests that gameful breathing training may help overcome this challenge. Objective This study aimed to introduce and evaluate the gameful breathing training app Breeze 2 and its novel real-time breathing detection algorithm that enables the interactive components of the app. Methods We developed the breathing detection algorithm by using deep transfer learning to detect inhalation, exhalation, and nonbreathing sounds (including silence). An additional heuristic prolongs detected exhalations to stabilize the algorithm’s predictions. We evaluated Breeze 2 with 30 participants (women: n=14, 47%; age: mean 29.77, SD 7.33 years). Participants performed breathing training with Breeze 2 in 2 sessions with and without headphones. They answered questions regarding user engagement (User Engagement Scale Short Form [UES-SF]), perceived effectiveness (PE), perceived relaxation effectiveness, and perceived breathing detection accuracy. We used Wilcoxon signed-rank tests to compare the UES-SF, PE, and perceived relaxation effectiveness scores with neutral scores. Furthermore, we correlated perceived breathing detection accuracy with actual multi-class balanced accuracy to determine whether participants could perceive the actual breathing detection performance. We also conducted a repeated-measure ANOVA to investigate breathing detection differences in balanced accuracy with and without the heuristic and when classifying data captured from headphones and smartphone microphones. The analysis controlled for potential between-subject effects of the participants’ sex. Results Our results show scores that were significantly higher than neutral scores for the UES-SF (W=459; P<.001), PE (W=465; P<.001), and perceived relaxation effectiveness (W=358; P<.001). Perceived breathing detection accuracy correlated significantly with the actual multi-class balanced accuracy (r=0.51; P<.001). Furthermore, we found that the heuristic significantly improved the breathing detection balanced accuracy (F1,25=6.23; P=.02) and that detection performed better on data captured from smartphone microphones than than on data from headphones (F1,25=17.61; P<.001). We did not observe any significant between-subject effects of sex. Breathing detection without the heuristic reached a multi-class balanced accuracy of 74% on the collected audio recordings. Conclusions Most participants (28/30, 93%) perceived Breeze 2 as engaging and effective. Furthermore, breathing detection worked well for most participants, as indicated by the perceived detection accuracy and actual detection accuracy. In future work, we aim to use the collected breathing sounds to improve breathing detection with regard to its stability and performance. We also plan to use Breeze 2 as an intervention tool in various studies targeting the prevention and management of noncommunicable diseases.

Background Slow-paced breathing has been shown to be positively associated with psychological and physiological health. In practice, however, there is little long-term engagement with breathing training, as shown by the usage statistics of breathing training apps. New research suggests that gameful smartphone-delivered breathing training may address this challenge. Objective This study assesses the impact of breathing training, guided by a gameful visualization, on perceived experiential and instrumental values and the intention to engage in such training. Methods A between-subject online experiment with 170 participants was conducted, and one-way multiple analysis of variance and two-tailed t test analyses were used to test for any difference in intrinsic experiential value, perceived effectiveness, and the intention to engage in either a breathing training with a gameful or a nongameful guidance visualization. Moreover, prior experience in gaming and meditation practices were assessed as moderator variables for a preliminary analysis. Results The intrinsic experiential value for the gameful visualization was found to be significantly higher compared to the nongameful visualization (P=.001), but there was no difference in either perceived effectiveness (P=.50) or the intention to engage (P=.44). The preliminary analysis of the influence of meditation and gaming experience on the outcomes indicates that people with more meditation experience yielded higher intrinsic experiential values from using the gameful visualization than people with no or little meditation experience (P=.03). This analysis did not find any additional evidence of gaming time or meditation experience impacting the outcomes. Conclusions The gameful visualization was found to increase the intrinsic experiential value of the breathing training without decreasing the perceived effectiveness. However, there were no differences in intentions to engage in both breathing training conditions. Furthermore, gaming and meditation experiences seem to have no or only a small positive moderating effect on the relationship between the gameful visualization and the intrinsic experiential value. Future longitudinal field studies are required to assess the impact of gameful breathing training on actual behavior, that is, long-term engagement and outcomes.

BACKGROUND Slow-paced breathing training can have beneficial effects on physiological and psychological well-being. Unfortunately, usage statistics indicate that adherence to breathing training apps is low. Recent work suggests that gameful breathing training may help to overcome this challenge. OBJECTIVE This study aims to introduce and evaluate Breeze 2, an updated version of the gameful breathing training app Breeze. It adds an improved appearance, a tutorial, the possibility to set specific training parameters, procedural generation of the visual biofeedback environment, and a novel real-time acoustic breathing detection algorithm that detects inhalation, exhalation, and non-breathing sounds (including silence). METHODS The breathing detection algorithm was developed using deep transfer learning and features an additional heuristic that prolongs detected exhalations to stabilize the algorithm's predictions. We evaluated Breeze 2 with thirty participants (14 females, Mage=29.77, SDage=7.33). Each participant performed two breathing training sessions with Breeze 2. In one session, the breathing detection used audio captured from headphones, and in the other session, it used audio from the smartphone's microphone. Participants answered questions regarding user engagement (UES-SF), perceived effectiveness (PE), perceived relaxation effectiveness (PRE), and perceived breathing detection accuracy (PDA). We used Wilcoxon-signed rank tests to compare UES-SF, PE, and PRE against neutral scores. To assess whether participants under- or overestimated the actual detection performance, we correlated PDA with the actual multi-class balanced accuracy and examined difference plots. We conducted a repeated-measures analysis of variance (ANOVA) to investigate the model's differences in balanced accuracy with and without the heuristic and when classifying data captured from headphones and smartphone microphones. To account for potential breathing sound differences between men and women, we controlled for between-subject effects of participants' gender. RESULTS Our results show scores significantly higher than neutral scores for UES-SF (W=459, P<.001), PE (W=465, P<.001), and PRE (W=358, P<.001). PDA correlated significantly with the multi-class balanced accuracy of the model (r=0.51, P<.001). Difference plots indicated that participants overestimated the model's performance when it performed poorly and underestimated it when it performed well. Furthermore, we found that the heuristic improved the model's balanced accuracy significantly (F(1,25)=10.25, P=.004) and that the model performed better on data captured from smartphone microphones than from headphones (F(1,25)=16.77, P<.001). We did not observe any significant between-subject effects of gender. The model alone reached a multi-class balanced accuracy of 74%. CONCLUSIONS Most participants perceived Breeze 2 as engaging and effective. Furthermore, the breathing detection worked well for most participants, as indicated by the perceived and true detection accuracy. In future work, we aim to use the collected breathing sounds to improve the breathing detection regarding its stability and performance. We also plan to employ Breeze 2 as an intervention tool in various studies targeting the prevention and management of NCDs.

BACKGROUND Slow-paced breathing trainings (6 breaths/min) improve physiological and psychological well-being by inducing relaxation characterized through an increased heart rate variability (HRV). However, classical breathing trainings have a limited target group and retention rates are very low. While a gamified approach may help to overcome these challenges, it is crucial to enable them in a scalable context (e.g., smartphone-only) and ensure a balance between experiential (user experience) and instrumental (increased HRV) values. Nevertheless, well-founded design principles (DPs) that lead to this outcome remain largely unexplored, especially in the context of mobile applications. OBJECTIVE This study aims to identify and evaluate DPs for mobile gamified biofeedback-guided breathing trainings that balance between instrumental and experiential values. METHODS Prior work was used to derive informed DPs, which, in turn, were applied to build the breathing training application Breeze. Then, through a pretest (N=3), mobile-specific design principles (M-DPs) have been formulated and Breeze adjusted accordingly. The DPs were then qualitatively evaluated in a pilot study (N=16). To ascertain that the instrumental values are maintained despite the introduction of gamified elements, recordings of breathing rates and HRV derived measures (e.g., RMSSD) were collected. For each participant, the recordings were then compared during baseline, a standard breathing training deployed on a smartphone, and Breeze. RESULTS Overall, 5 DPs were identified that propose to use cool colors, natural settings, tightly incorporated gamified elements, game mechanics that reflect physiological measures, and a light narrative and progression model. Moreover, 2 M-DPs were proposed that focus on the visual accentuation and alignment of guidance and biofeedback elements. Also, Breeze was effective as it resulted in a slow-paced breathing rate of 6 breaths per minute, which, in turn, resulted in significantly increased HRV measures compared to baseline (P<.001 for RMSSD). CONCLUSIONS The implemented DPs have a positive resonance in respondents but require further substantiation. Nevertheless, when compared to a standard breathing training, they lead to an increase in experiential value while maintaining the instrumental value.