Hierarchical amplitude modulation structures and rhythm patterns: Comparing Western musical genres, song, and nature sounds to Babytalk

Abstract

Statistical learning of physical stimulus characteristics is important for the development of cognitive systems like language and music. Rhythm patterns are a core component of both systems, and rhythm is key to language acquisition by infants. Accordingly, the physical stimulus characteristics that yield speech rhythm in “Babytalk” may also describe the hierarchical rhythmic relationships that characterize human music and song. Computational modelling of the amplitude envelope of “Babytalk” (infant-directed speech, IDS) using a demodulation approach (Spectral-Amplitude Modulation Phase Hierarchy model, S-AMPH) can describe these characteristics. S-AMPH modelling of Babytalk has shown previously that bands of amplitude modulations (AMs) at different temporal rates and their phase relations help to create its structured inherent rhythms. Additionally, S-AMPH modelling of children’s nursery rhymes shows that different rhythm patterns (trochaic, iambic, dactylic) depend on the phase relations between AM bands centred on ~2 Hz and ~5 Hz. The importance of these AM phase relations was confirmed via a second demodulation approach (PAD, Probabilistic Amplitude Demodulation). Here we apply both S-AMPH and PAD to demodulate the amplitude envelopes of Western musical genres and songs. Quasi-rhythmic and non-human sounds found in nature (birdsong, rain, wind) were utilized for control analyses. We expected that the physical stimulus characteristics in human music and song from an AM perspective would match those of IDS. Given prior speech-based analyses, we also expected that AM cycles derived from the modelling may identify musical units like crotchets, quavers and demi-quavers. Both models revealed an hierarchically-nested AM modulation structure for music and song, but not nature sounds. This AM modulation structure for music and song matched IDS. Both models also generated systematic AM cycles yielding musical units like crotchets and quavers. Both music and language are created by humans and shaped by culture. Acoustic rhythm in IDS and music appears to depend on many of the same physical characteristics, facilitating learning.

Temporal dynamics of statistical learning in children’s song contributes to phase entrainment and production of novel information in multiple cultures

“…half notes), while the lower hierarchy corresponds to a frequency band (approximately, 3 Hz <) of syllable and musical notes (e.g. quarter notes) 19 . Such a hierarchical structure of rhythm can be visualized from the AM envelope of sound waveforms 20 , 21 .…”

“…quarter notes) around 4–12 Hz while the higher hierarchy corresponds to prosody, intonation, and long musical notes (e.g. half notes) around 1–3 Hz 19 . There are also rhythms around 12–30 Hz that correspond to phonemes or sound onsets at even lower levels of the hierarchy.…”

“…has demonstrated that the slower band hierarchy (i.e. 1–3 Hz) is especially pronounced in children's songs and speech directed towards infants and children (referred to as infant/child-directed speech) compared to other types of sounds 19 and in at least one other culture 30 . Further, evidence has shown that the infant is first phase-entrained to the slower rhythm (1–3 Hz) in infant-directed speech 31 , suggesting that such a slower-band rhythm is important for early learning of language and music.…”

Situating language and music research in a domain‐specific versus domain‐general framework: A review of theoretical and empirical data

“…The frequency of modulations in amplitude has been investigated, especially with an interest in the neural responses to the speech/musical signal. The spectrum of speech modulation (for a small set of language corpora) ranges between 4 and 5 Hz, while the spectrum of music modulation (for a small set of musical genres) ranges from 1 to 2 Hz (Daikoku & Goswami, 2022; Ding, Patel, et al., 2017). The similarities in metrical organisation and the assumption that critical information is conveyed in specific time frequencies (Pellegrino et al., 2011) have increasingly motivated the investigation of the relationship between metre perception and attention.…”

Non-autistic persons modulate their speech rhythm while talking to autistic individuals

“…Such phonological hierarchy can be identified from the amplitude modulation (AM) hierarchy of speech waveforms below approximately 40 Hz. Evidence has shown that AM hierarchy of speech signals, including prosody, syllable, and phoneme, can be detected in the temporal structure of speech waveforms such as delta (<4 Hz), theta/alpha (4-12 Hz), and beta/ low gamma , respectively, regardless of speech types [13,18]. Linguistically, such phonological hierarchy is classically represented as a tree that captures the relative prominence of units [16,19] (Fig 1).…”