# Synthesis of Non-Linguistic Utterances for Sound Design Support Using a Genetic Algorithm
### Related Work on Interpretation of NLUs
The pitch range of the sounds spanned from 100 Hz to 1500 Hz, aligning with the typical range for human communication. Overall, the study concluded that non-verbal sounds (NLUs) hold promise for effective human-robot communication, with 55% of participants successfully recognizing the sounds for intentions, and 80% recognizing the sounds for emotions. [PERSON] et al. [15] developed a model to infer the valence and arousal of 560 NLUs extracted from popular movies, TV shows, and video games. Three sets of audio features, which included combinations of spectral energy, spectral spread, zero-crossing rate (ZCR), mel frequency cepstral coefficients (MFCCs), audio chroma, pitch, jitter, formant, shimmer, loudness, and harmonics-to-noise ratio, were used. These features were extracted from the sounds and, after feature reduction where applicable, the best-performing models used a random forest regressor and inferred emotional valence with a mean absolute error (MAE) of 0.107 and arousal with an MAE of 0.097. The correlation coefficients between predicted and actual valence and arousal were 0.63 and 0.75, respectively. This random forest regression model is used in the current work as well and is referred to repeatedly in this paper. [PERSON] et al. [16] applied coding rules based on animal calls and vocalizations in the design of NLUs for social robots. They synthesized their sounds using sine waves as a basis and progressively altered the pitch, duration, harmonics, and timbre to modify them. They carried out experiments where they verified that humans could recognize the emotions conveyed by the sounds. They also suggested that sounds with higher frequencies corresponded to higher arousal, while shorter sounds corresponded to a positive valence. The researchers used a linear mixed model to infer the valence and arousal of the sounds and obtained correlation coefficients between 0.5 and 0.6 for both. [PERSON] [17] showed that NLUs could convey positive and negative affect using simple sine tones with rising, falling, or flat frequency gradients. Rising frequency gradients corresponded to positive emotions while falling frequency gradients corresponded to negative emotions. The same patterns were reported in studies on sounds known as earcons, which are everyday tones and sounds used in computers, mobile phones, and other machines to signal basic feedback to the user, such as task completion, notifications, or warnings [18]. [PERSON] [19] also suggested that NLUs help to manage the expectations of users of social robots. [PERSON] et al. [20] explored the effect of musical prosody on interactions between humans and groups of robots. They introduced the concept of entitativity, meaning the perception of the group as being a single entity, and found that alterations of prosodic features of musical sounds increased both the likeability and trust of the robots as well as the entitativity. The results of this study suggest that NLUs can also improve interactions between humans and groups of robots. These studies demonstrate the potential of NLUs in expressing emotions and affect for social robotics. NLUs hold promise for applications involving uncomplicated robots, robotic companions, or toys that do not need intricate speech capabilities, instead relying on endearing or subtle sounds made feasible by NLUs. Furthermore, NLUs possess language-agnostic qualities, allowing for swift and efficient communication of messages. They can be understood by both children and adults, effectively conveying fundamental emotions such as joy, sadness, anger, and fear. The interpretation of NLUs can be influenced by situational context, and they can function as an additional modality of communication alongside speech and visual cues [12; 13; 21]. Insights into the expressive capacities of emotions have been gleaned from analyzing the sounds and speech of robots featured in popular films. When designing NLUs for social robots, crucial factors to consider encompass intonation, pitch, timbre, and communicative movements.