Chirag Gokani – chiragokani@utexas.edu Instagram: @chiragokani Applied Research Laboratories and Walker Department of Mechanical Engineering Austin, Texas 78766-9767
Preston S. Wilson (also at Applied Research Laboratories and Walker Department of Mechanical Engineering)
Popular version of 2aMU6 – Timbral effects of the right-hand techniques of jazz guitarists Wes Montgomery and Joe Pass Presented at the 188th ASA Meeting Read the abstract at https://doi.org/10.1121/10.0037556
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Wes Montgomery and Joe Pass are two of the most influential guitarists of the 20th century. Acclaimed music educator and producer Rick Beato says,
Wes influenced all my favorite guitarists, from Joe Pass, to George Benson, to Pat Martino, to Pat Metheny, to John Scofield. He influenced Jimi Hendrix, he influenced Joe Satriani, Eric Johnson. Virtually every guitarist I can think of that I respect, Wes is a major, if not the biggest, influence of.
Beato similarly praises Joe Pass for his 1973 album Virtuoso, calling it the “album that changed my life”:
If there’s one record that I ever suggest to people that want to get into jazz guitar, it’s this record, Joe Pass, Virtuoso.
Part of what made Wes Montgomery and Joe Pass so great was their iconic guitar tone. Montgomery played with his thumb, and his tone was focused and warm. See, for example, “Cariba” from Full House (1962). Meanwhile, Pass played both fingerstyle and with a pick, and his tone was smooth and rich. His fingerstyle playing can be heard on “Just Friends” from I Remember Charlie Parker (1979), and his pick playing can be heard on “Dreamer (Vivo Sonhando)” from Ella Abraca Jobim (1981).
Wes Montgomery (left, Tom Marcello, CC BY-SA 2.0) and Joe Pass (right, Chuck Stewart, Public domain via Wikimedia Commons)
To better understand the tone of Montgomery and Pass, we modeled the thumb, fingers, and pick as they interact with a guitar string.
Our model for how the thumb, fingers, and pick excite a guitar string. The string’s deformation is exaggerated for the purpose of illustration.
One factor in the model is the location at which the string is excited. Montgomery played closer to the bridge of the guitar, while Pass played closer to the neck. Another important factor is the amount that the thumb, fingers, and pick slip off the string. Montgomery’s thumb delivered a “pluck” and slipped less than Pass’s pick, which delivered more of a “strike” to the string.
Simulations of the model suggest that Montgomery and Pass balanced these two factors with the choice of thumb, fingers, and pick. The focused nature of Montgomery’s tone is due to his thumb, while the warmth of his tone arises from playing closer to the bridge and predominantly plucking the string. Meanwhile, the richness of Pass’s tone is due to his pick, while its smooth quality is due to playing closer to the neck and predominantly striking the string. Pass’s fingerstyle playing falls in between the thumb and pick techniques.
Guitarists wishing to play in the style of Montgomery and Pass can adjust their technique to match the parameters of our model. Conversely, the parameters of our model can be adjusted to emulate the tone of other notable guitarists.
Notable jazz and fusion guitarists grouped by technique. The parameters of our model can be adjusted to describe these guitarists.
Our model could also be used to synthesize realistic digital guitar voices that are more sensitive to the player’s touch.
To demonstrate the effects of the right-hand technique on the tone, we offer an arrangement of the jazz standard “Stella by Starlight” for solo guitar. The thumb is used at the beginning of the arrangement, with occasional contributions from the fingers. The fingers are used exclusively from 0:50-1:10, after which the pick is used to conclude the arrangement. Knowledge of the physics underlying these techniques helps us better appreciate both the subtlety of guitar performance and the contributions of Montgomery and Pass to music.
Andrew Brian Horner horner@cse.ust.hk
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR
Popular version of 1aMU2 – The emotional characteristics of the violin with different pitches, dynamics, and vibrato levels
Presented at the 187th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0034939
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Music has a unique way of moving us emotionally, but have you ever wondered how individual sounds shape these feelings?
In our study, we looked at how different features of violin notes—like pitch (the height of the notes), dynamics (the loudness of the sounds), and vibrato (how the note vibrates)—combine to create emotional responses. While previous research often focuses on each feature in isolation, we explored how they interact, revealing how the violin’s sounds evoke specific emotions.
To conduct this study, we used single-note recordings from the violin at different pitches, two levels of dynamics (loud and soft), and two vibrato settings (no vibrato and high vibrato). We invited participants to listen to these sounds and rate their emotional responses using a scale of emotional positivity (valence) and intensity (arousal). Participants also selected which emotions they felt from a list of 16 emotions, such as joyful, nervous, relaxed, or agitated.
Audio 1. The experiment used a violin single-note sample (middle C pitch + loud dynamics + no vibrato).
Audio 2. The experiment used a violin single-note sample (middle C pitch + soft dynamics + no vibrato).
Audio 3. The experiment used a violin single-note sample (middle C pitch + loud dynamics + high vibrato).
Audio 4. The experiment used a violin single-note sample (middle C pitch + loud dynamics + high vibrato).
Our findings reveal that each element plays a unique role in shaping emotions. As shown in Figure 1, higher pitches and strong vibrato generally raised emotional intensity, creating feelings of excitement or tension. Lower pitches were more likely to evoke sadness or calmness, while loud dynamics made emotions feel more intense. Surprisingly, sounds without vibrato were linked to calmer emotions, while vibrato added energy and excitement, especially for emotions like anger or fear. And Figure 2 illustrates how strong vibrato enhances emotions like anger and sadness, while the absence of vibrato correlates with calmer feelings.
Figure 1. Pitch, Dynamics, and Vibrato average ratings on valence-arousal with different levels. It shows that higher pitches and strong vibrato increase arousal, while soft dynamics and no vibrato are linked to higher valence, highlighting pitch as the most influential factor.
Figure 2. Pitch, Dynamics, and Vibrato average ratings on 16 emotions. It shows that strong vibrato enhances angry and sad emotions, while no vibrato supports calm emotions; higher pitches increase arousal for angry emotions, and brighter tones evoke calm and happy emotions.
Our research provides insights for musicians, composers, and even music therapists, helping them understand how to use the violin’s features to evoke specific emotions. With this knowledge, violinists can fine-tune their performance to match the emotional impact they aim to create, and composers can carefully select sounds that resonate with listeners’ emotional expectations.
The University of British Columbia, Department of Linguistics, Vancouver, British Columbia, V6T 1Z4, Canada
Additional authors:
Sydney Norris, Sabrina Luk, Marcell Maitinsky, Md Jahurul Islam, and Bryan Gick
Popular version of 3pPP6 – The Role of Genre Association in Sung Dialect Categorization
Presented at the 187th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0035323
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Have you ever listened to a song and later been surprised to hear the artist speak with a different accent than the one you heard in the song? Take country singer Keith Urban’s song “What About Me” for instance; when listening, you might assume that he has a Southern American (US) English accent. However, in his interviews, he speaks with an Australian English accent. So why did you think he sounded Southern?
Research suggests that specific accents or dialects are associated with musical genres [2], that singers adjust their accents based on genre [4]; and that foreign accents are more difficult to recognize in songs compared to speech [5]. However, when listeners perceive an accent in a song, it is unclear which type of information they rely on: the acoustic speech information or information about the musical genre. Our previous research investigated this question for Country and Reggae music and found that genre recognition may play a larger role in dialect perception than the actual sound of the voice [9].
Our current study explores American Blues and Folk music, genres that allow for easier separation of vocals from instrumentals, with more refined stimuli manipulation. Blues is strongly associated with African American English [3], while Folk can be associated with a variety of (British, American, etc.) dialects [1]. Participants listened to manipulated clips of sung and “spoken” lines taken from songs in both genres, which were transcribed for participants (see Figure 1). AI applications were used to remove instrumentals for both sung and spoken clips, while “spoken” clips also underwent rhythm and pitch normalization so that they sounded like spoken rather than sung speech. After hearing each sung or spoken line, participants were asked to identify the dialect they heard from six options [7, 8] (see Figure 2).
Figure 1: Participant view of a transcript from a Folk song clip.
Figure 2: Participant view of six dialect options after hearing a clip.
Participants were much more confident and accurate in categorizing accents for clips in the Sung condition, regardless of genre. The proportion of uncertainty (“Not Sure” responses) in the Spoken condition was consistent across genres (see “D” in Figure 3), suggesting that participants were more certain of dialect when musical cues were present. Dialect categories followed genre expectations, as can be seen from the increase in identifying African American English for Blues in the Sung condition (see “A”). Removing uncertainty by adding genre cues did not increase the likelihood of “Irish English” or “British English” being chosen for Blues, though it did for Folk (see “B” and “C” in Figure 3), in line with genre-based expectations.
Figure 3: Participant dialect responses.
These findings enhance our understanding of the relationship between musical genre and accent. Referring again to the example of Keith Urban, the singer’s stylistic accent change may not be the only culprit for our interpretation of a Southern drawl. Rather, we may have assumed we were listening to a musician with a Southern American English Accent when we heard the first banjo-like twang or tuned into iHeartCountry Radio. When we listen to a song and perceive a singer’s accent, we are not only listening to the sounds of their speech, but are also shaping our perception from our expectations of dialect based on the musical genre.
References:
Carrigan, J., Henry L. (2004). Lornell, kip. the NPR curious listener’s guide to american folk music. Library Journal (1976), 129(19), 63.
De Timmerman, Romeo, et al. (2024). The globalization of local indexicalities through music: African‐American English and the blues. Journal of Sociolinguistics, 28(1), 3–25. https://doi.org/10.1111/josl.12616.
Gibson, A. M. (2019). Sociophonetics of popular music: insights from corpus analysis and speech perception experiments [Doctoral dissertation, University of Canterbury]. http://dx.doi.org/10.26021/4007.
Mageau, M., Mekik, C., Sokalski, A., & Toivonen, I. (2019). Detecting foreign accents in song. Phonetica, 76(6), 429–447. https://doi.org/10.1159/000500187.
RStudio. (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/.
Stoet, G. (2010). PsyToolkit – A software package for programming psychological experiments using Linux. Behavior Research Methods, 42(4), 1096-1104.
Stoet, G. (2017). PsyToolkit: A novel web-based method for running online questionnaires and reaction-time experiments. Teaching of Psychology, 44(1), 24-31.
Walter, M., Bengtson, G., Maitinsky, M., Islam, M. J., & Gick, B. (2023). Dialect perception in song versus speech. The Journal of the Acoustical Society of America, 154(4_supplement), A161. https://doi.org/10.1121/10.0023131.
Department of Department of Computer Science and Engineering
The Hong Kong University of Science and Technology
Hong Kong SAR
Andrew Brian Horner horner@cse.ust.hk
Department of Department of Computer Science and Engineering
The Hong Kong University of Science and Technology
Hong Kong SAR
Popular version of 1aMU3 – Emotional characteristics of the erhu and violin: a comparative study of emotional intensity in musical excerpts
Presented at the 187th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0034940
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Music speaks to us across cultures, but can the instruments we choose shape our emotions in different ways?
This study compares the emotional responses evoked by two similar yet culturally distinct string instruments: the Chinese erhu and the Western violin. Both are bowed string instruments, but they have distinct sounds and cultural roles that could lead listeners to experience different emotions. Our research focuses on whether these instruments, along with variations in performance and listener familiarity, influence emotional intensity in unique ways.
Western violin performance example: violinist Ray Chan playing ‘Mendelssohn Violin Concerto in E minor, Op. 64’
Chinese erhu performance example: erhu player Guo Gan playing the Chinese piece ‘Horse Racing’ (feat. Pianist Lang Lang)
To explore these questions, we conducted three online listening experiments. Participants were asked to listen to a series of short musical pieces performed on both the erhu and violin. They then rated each piece using two emotional measures: specific emotion categories (such as happy, sad, calm, and agitated) and emotional positivity and intensity.
Our results show clear emotional differences between the instruments. The violin often evokes positive, energetic emotions, which may be due to its bright tone and dynamic range. By contrast, the erhu tends to evoke sadness, possibly because of its softer timbre and its traditional association with melancholy in Chinese music.
Interestingly, familiarity with the instrument played a significant role in listeners’ emotional responses. Those who were more familiar with the violin rated the pieces as more emotionally intense, suggesting that cultural background and previous exposure shape how we emotionally connect with music. However, our analysis also found that different performances of the same piece generally did not change emotional ratings, emphasizing that the instrument itself is a major factor in shaping our emotional experience.
These findings open new paths for understanding how cultural context and personal experiences influence our emotional reactions to music. The distinct emotional qualities of the erhu and violin reveal how musical instruments can evoke different emotional responses, even when playing the same piece.
Popular version of 1aMU8 – Effect of years of voice training on chest and head register tongue shape variability
Presented at the 187th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0034945
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Imagine being in a voice lesson, and as you try to hit a high note, your voice coach says, “suppress your tongue” or “pretend your tongue doesn’t exist!” What does this mean, and why do singers do this?
One vocal technique used by professional singers is to sing in different vocal registers. Generally, a man’s natural speaking voice and the voice people use to sing lower notes is called the chest voice—you can feel a vibration in your chest if you place your hand over it as you vocalize. When moving to higher notes, singers shift to their head voice, where vibrations feel stronger in the head. However, what role does the tongue play in this transition? Do all singers, including amateurs, naturally adjust their tongue when switching registers, or is this adjustment a learned skill?
Figure 1: Approximate location of feeling/sensation for chest and head voice.
We are interested in vowels and the pitch range during the passaggio, which is the shift or transition point between different vocal registers. The voice is very unstable and prone to audible cracking during the passaggio, and singers are trained to navigate it smoothly. We also know that different vowels are produced in different locations in the mouth and possess different qualities. One way that singers successfully navigate the passaggio is by altering the vowel through slight adjustments to tongue shape. To study this, we utilized ultrasound imaging to monitor the position and shape of the tongue while participants with varying levels of vocal training sang vowels across their pitch range, similar to a vocal warm-up.
Video 1: Example of ultrasound recording
The results indicated that, in head voice, the tongue is generally positioned higher in the mouth than in chest voice. Unsurprisingly, this difference is more pronounced for certain vowels than for others.
Figure 2: Tongue position in chest and head voice for front and back vowel groups. Overlapping shades indicate that there is virtually no difference.
Singers’ tongues are also shaped by training. Recall the voice coach’s advice to lower your jaw and tongue while singing—this technique is employed to create more space in the mouth to enhance resonance and vocal projection. Indeed, trained singers generally have a lower overall tongue position.
As professional singers’ transitions between registers sound more seamless, we speculated that trained singers would exhibit smaller differences in tongue position between registers than untrained singers, who have less developed tongue control. In fact, it turns out that the opposite is true: the tongue behaves differently in chest voice and head voice, but only for individuals with vocal training.
Figure 3: Tongue position in chest and head voice for singers with different levels of training.
In summary, our research suggests that tongue adjustments for register shifts may be a learned technique. The manner in which singers adjust their tongues for different vowels and vocal registers could be an essential component in achieving a seamless transition between registers, as well as in the effective use of various vocal qualities. Understanding the interactions among vowels, registers, and the tongue provides insight into the mechanisms of human vocal production and voice pedagogy.
Figure 1. Pitch, Dynamics, and Vibrato average ratings on valence-arousal with different levels. It shows that higher pitches and strong vibrato increase arousal, while soft dynamics and no vibrato are linked to higher valence, highlighting pitch as the most influential factor.
Figure 2. Pitch, Dynamics, and Vibrato average ratings on 16 emotions. It shows that strong vibrato enhances angry and sad emotions, while no vibrato supports calm emotions; higher pitches increase arousal for angry emotions, and brighter tones evoke calm and happy emotions.
Figure 1: Participant view of a transcript from a Folk song clip.
Figure 2: Participant view of six dialect options after hearing a clip.
Figure 3: Participant dialect responses.
Figure 1: Approximate location of feeling/sensation for chest and head voice.
Figure 2: Tongue position in chest and head voice for front and back vowel groups. Overlapping shades indicate that there is virtually no difference.
Figure 3: Tongue position in chest and head voice for singers with different levels of training.