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Sounds associated with aurora borealis, so called Auroral Sounds (AS), are explained by electrical discharges occurring in Temperature Inversion Layer (TIL) approximately 75 meters above the ground. It is assumed that under favorable weather conditions the number of AS events depends on the activity of the geomagnetic (GM) storm. The historical testimonies of AS indicate that the GM storm should be strong enough to rise the bright and lively auroras to the zenith before these sounds can be observed. The goal of this study is to test the relevancy of this claim by studying the sensitivity of the AS producing mechanism under a moderate GM activity. A four-hour long period of the sound measurements done around the local magnetic midnight Jan 25–26, 2022 at Fiskars village, Finland, is analyzed. Sixty AS event candidates were manually selected and their temporal distribution and period histogram constructed for statistical comparisons with the GM data measured simultaneously by the Finnish Meteorological Institute (FMI) at Nurmijärvi Geophysical Observatory (NGO). The data was also used for regression models to study possible causal relationship between the GM activity and the AS events. A strong causality between the GM activity and the AS events was found. The sounds were predicted by the GM activity with 90% accuracy after a delay of 21 min. The results show that the AS events are much more common than previously thought.

Puheen ja äänen tuottoa tarkastellaan usein yhä edelleen perinteisen nelijakomallin mukaan, jossa hengityselimistö, äänihuulet, ääntöväylä ja huuliaukko toimivat itsenäisinä yksiköinä. Todellisuudessa kuitenkin ääntöväylän muodostama resonaattori on vuorovaikutuksessa molempien päätepisteidensä, äänihuulten ja huuliaukon, kanssa. Tässä katsauksessa kuvataan keskeiset vuorovaikutuksen muodot ja annetaan esimerkkejä niiden toteutumisesta. Interaktion voidaan todeta toteutuvan niin ääniharjoitteissa, taiteellisessa äänenkäytössä kuin äänihäiriöissäkin. Käytännön esimerkit opastavat myös tunnistamaan, miltä interaktion toteutuminen tuntuu kehossa., Speech and voice production is still often seen through a traditional model with four independent components: respiratory system, vocal folds, vocal tract and the lips. In reality, however, the resonator, formed by the vocal tract, is in interaction with both of its endings: the vocal folds and the lips. This article makes a review on the main types of interaction and gives examples of them. Through the examples it is possible to state how the interaction takes place in voice exercises and therapy methods, artistic voice use and voice disorders. Practical examples help to find out how the interaction feels in the body.

Automatic detection and classification of short and nonstationary events in noisy signals is widely considered to be a difficult task for traditional frequency domain and even time–frequency domain approaches. A novel method for audio signal classification is introduced. It is based on statistical properties of the temporal fine structure of audio events. Artificially generated random signals and unvoiced stop consonants of speech are used to evaluate the method. The results show improved recognition accuracy in comparison to traditional approaches.

Despite large-scale research, development of robust machines for imitation and inversion of human speech into articulatory movements has remained an unsolved problem. We propose a set of principles that can partially explain real infants' speech acquisition processes and the emergence of imitation skills and demonstrate a simulation where a learning virtual infant (LeVI) learns to invert and imitate a virtual caregiver's speech. Based on recent findings in infants' language acquisition, LeVI learns the phonemes of his native language in a babbling phase using only caregiver's feedback as guidance and to map acoustically differing caregiver's speech into its own articulation in a phase where LeVI is imitated by the caregiver with similar, but not exact, utterances. After the learning stage, LeVI is able to recognize vowels from the virtual caregiver's VCVC utterances perfectly and all 25 Finnish phonemes with an average accuracy of 88.42%. The place of articulation of consonants is recognized with an accuracy of 96.81%. LeVI is also able to imitate the caregiver's speech since the recognition occurs directly in the domain of articulatory programs for phonemes. The learned imitation ability (speech inversion) is strongly language dependent since it is based on the phonemic programs learned from the caregiver. The findings suggest that caregivers' feedback can act as an important signal in guiding infants' articulatory learning, and that the speech inversion problem can be effectively approached from the perspective of early speech acquisition.