Sound Ecology and Acoustic Health, Part 4: Time Domain Analysis
Notice bibliographique
Résumé
Introduction to periodical article: We have spent the last while working towards a mobile phone application to help identify a local noise nuisance problem. We joked with Mike’s neighbour’s kids that the record and play-back .3GPP file WAT_AN_APP application, Fig. 1A, was to impress them that “Without Any Teenage Assistance Necessary, we could write an Android APP” CC Issues ###. We then added just enough additional code (JEAC) to store an audio record for later analysis CC Issues ###. To continue the friendly tease in CC Issues ###, we pretended that the project code was actually designed to detect – “Things that Go BOOm at Night+ - how many “TGBN ghosts” there are in the neighbourhood (Fig. 1B). As they say “Be careful what you wish for!” Our neighbours got interested in the community noise issues we were really trying to measure. They had their teenagers explore the acoustic health of their home using our work-in-progress. Late yesterday, a knock on the door revealed our neighbours asking for help. Their eldest teenager had gone to the University of Pennsylvania. According to the Penn Arts and Sciences website sites.sas.upenn.edu/ghosts-healing, a group of scholars from literature, art history, nursing, archaeology, religious studies, science and medicine wants to take research on ghosts seriously. So our neighbour’s kid decided to volunteer with this group. This turned into a term project -- working on analysing room acoustics as a possible source of “that friendly spectral feeling”. Hence the frantic email message they wanted to pass on -- Term’s nearly over! Could you please get Mike to hurry up and fulfill his promise in that first CC article of providing enough information to do some “real” digital signal processing (DSP) analysis? While he was at it – could he get Adrien to add some graphics’ capability to display the frequency characteristics of the sounds in a room to make my term report more interesting! In Canada, it always good to keep on the right side of the neighbour’s kids as they are a good (inexpensive) labour source for shovelling snow off sidewalks. So we decide to write a RoomAcoustics Analysis Capability addition. Actually we wanted to be able to say that we had Penn-ed some code (sorry for the pun ( :-). ). First we will explain how to reliably excite a room resonance that can be captured by our existing TGBN detector code. We will graphically display the room audio signal to give us a first chance to compare resonance characteristics in different rooms. We found that looking for small differences in the captured signals displayed as a function of time meant working (slowly) with a lot of data. So we added a way to generate frequency information signal of captured signals using a discrete Fourier transform (DFT) algorithm code we grabbed from the web. Fig 2A shows the background noise recorded in our university lab. Having noticed a possible small 727 Hz ghost sleeping next to our desk, we tried to move around the room to better record its characteristic, Fig. 2B. The frequency characteristics of our two records look too similar for us to be sure that we have a non-snoring ghost close by. We decided to wake it up by outputting a three second Chirp, a sound burst from 50 to 1000 Hz. Fig. 2C show the frequency response of the Chirp signal, but there is not much there other than measuring the poor low frequency of our phone’s speaker. However, we accidently got close enough that we woke up the sleeping ghost which significantly changed the frequency response of the room, Fig 2D.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».