ارائه یک مدل پارامتریک تطبیقی جهت کشف و رده‌بندی وقایع صوتی در سیگنال‌های محیطی

نوع مقاله : علمی-پژوهشی

نویسندگان

دانشکده مهندسی کامپیوتر و فناوری اطلاعات - دانشگاه صنعتی شاهرود

چکیده

کشف وقایع صوتی در محیط کار و زندگی یک نیاز مدرن جهت گردآوری اطلاعات است. تاکنون بیشتر تحقیق‌ها بر واقعه صوتی خاص و یا تعداد محدودی از وقایع صوتی برجسته متمرکز بوده‌است. در اینجا یک مدل‌سازی جدید جهت کشف تمام وقایع صوتی رخ‌داده در رکورد و تعیین محدوده زمانی برای هر یک از آن‌ها ارائه شده‌است. نوآوری شامل مدل‌سازی جدید همراه با پارامترهای تطبیقی در مدل است. پس از استخراج ویژگی‌ها و تعیین مقادیر دو پارامتر آلفا و بتا از دو قطعه‌بندی مجزا و ترکیب خروجی آن‌ها برای تعیین وقایع صوتی و محدوده زمانی آن‌ها استفاده شده‌است. این وقایع جهت رده‌بندی به الگوریتم KNN فرستاده می‌شوند. پارامترها امکان دقت بیشتر و یا میزان کشف حداکثری را ممکن می‌سازند. وقایع صوتی آزمایش‌شده شامل 16 نوع صدای اتاق کار اداری هستند که برخی شبیه هم و بعضی نیز مشابه نویز محیط هستند. در سنجش عملکرد برحسب واقعه، میزان درستی کشف 70.1 درصد، فراخوانی 75.8 درصد و میزان F1، 72.8 درصد بوده‌است. همچنین میزان F1 برحسب فریم 80.6 درصد حاصل شد. مقدار F1 برحسب واقعه، نسبت به قبل 10.8% بهبود داشته‌است که مویدکارآمدی مدل پیشنهادی است.

کلیدواژه‌ها

عنوان مقاله [English]

Providing an Adaptive Model with two Adjustable Parameters for Audio Event Detection and Classification in Environmental Signals

نویسندگان [English]

  • M. Derakhshan
  • H. Marvi
  • H. Hassan poor
Computer and IT Engineering Department, Shahrood University of Technology, Shahrood, Iran
چکیده [English]

Audio event detection (AED) is a modern way to collect data about human activities in the workplace or in other life environments. We proposed a novel adaptable model based on using two parameters, α and ᵦ to detect all audio events that may be present in a given record accompanied by their time limits in which they occur. After feature extraction and setting the values of the two key parameters, alpha and beta, the audio sequence will be sent into two distinct sub-systems for event detection. The outputs from the two sub-classifiers are then combined and necessary refinements are made on the event time limits. The final detected events are sent to the KNN classifier. The parameters serve as a trade-off tool between precision and recall expectation in the detection process. In the tests, 16 different audio events of an office room were detected, some being similar to each other and some have very similar characteristics to those of the background noise. At frame-based (FB) level, the precision rate was 70.1%, the rate of recall was 75.8%, and F1-measure was 72.8%. The F1-measure has increased by 10.8% suggesting promising applications of the model.

کلیدواژه‌ها [English]

  • Audio event detection (AED)
  • environmental sounds
  • unsupervised learning
  • adaptable modeling systems
  • audio monitoring systems
  • audio-based acquisition systems

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