An Enhanced Temporal Feature Integration Method for Environmental Sound Recognition

Abstract

Temporal feature integration refers to a set of strategies attempting to capture the information conveyed in the temporal evolution of the signal. It has been extensively applied in the context of semantic audio showing performance improvements against the standard frame-based audio classification methods. This paper investigates the potential of an enhanced temporal feature integration method to classify environmental sounds. The proposed method utilizes newly introduced integration functions that capture the texture window shape in combination with standard functions like mean and standard deviation in a classification scheme of 10 environmental sound classes. The results obtained from three classification algorithms exhibit an increase in recognition accuracy against a standard temporal integration with simple statistics, which reveals the discriminative ability of the new metrics.

Semantic Crowdsourcing of Soundscapes Heritage: A Mojo Model for Data-Driven Storytelling

“…Context-and location-aware services can be combined with (multichannel) semantic processing to offer spatiotemporal sound mapping and pattern-related visualizations. Such featured summarization techniques are encountered on generic audio detection and classification tasks, including environmental sound recognition [14,[49][50][51][52][53][54][55][56][57][58][59]. In this view, crowdsourced audio data can offer soundscape enhancement with multiple augmentation layers in favor of documentation, data-driven storytelling, and management.…”

“…In this view, crowdsourced audio data can offer soundscape enhancement with multiple augmentation layers in favor of documentation, data-driven storytelling, and management. The massive research progress on the domain has established multiple pattern recognition schemes and hierarchical semantic audio taxonomies to describe the sound-fields associated with the different social events [13][14][15][16][17][18][19][20][21][22][23][24][52][53][54][55][56][57][58][59]. Apart from the geographical-and time-related information that a mobile terminal can easily hold, environmental sounds and soundscapes can be classified, filtered, and highlighted based on the associated pattern classification taxonomies, various low-level audio descriptors, other semantic labels concerning the transmitted or perceived emotions, etc.…”

“…Apart from the geographical-and time-related information that a mobile terminal can easily hold, environmental sounds and soundscapes can be classified, filtered, and highlighted based on the associated pattern classification taxonomies, various low-level audio descriptors, other semantic labels concerning the transmitted or perceived emotions, etc. [49][50][51][52][53][54][55][56]60,61]. Furthermore, recent audio and audiovisual captioning trends can offer additional semantic conceptualization meta-data [62][63][64][65].…”

“…A linked popular research topic that significantly propelled multidisciplinary scientific projects and associated knowledge gain is the way of learning by example, through the Machine and Deep Learning (ML/DL) paradigms. Both the audio semantics and the CH domains have also benefited the made breakthroughs and progress [4][5][6][7][13][14][15][16][52][53][54][55][56][57][58][59]. Hence, sound and acoustic scene recordings can be processed to provide event detection and recognition outcomes, offering pattern-related metadata, content-based description, and management automation (i.e., retrieval, summarization/highlighting, etc.).…”

Investigation of Spoken-Language Detection and Classification in Broadcasted Audio Content

“…In this context, new audio recognition and semantic analysis techniques are deployed for General Audio Detection and Classification (GADC) tasks, which are very useful in many multidisciplinary domains [4][5][6][7][8][9][10][11][12][13][14][15][16]. Typical examples include speech recognition and perceptual enhancement [5][6][7][8], speaker indexing and diarization [14][15][16][17][18][19], voice/music detection and discrimination [1][2][3][4][9][10][11][12][13][20][21][22], information retrieval and genre classification of music [23,24], audio-driven alignment of multiple recordings [25,26], sound emotion recognition [27][28][29] and others [10,[30][31][32]. Concerning the media production and broadcasting domain, audio and audio-driven segmentation allow for the implementation of prope...…”