Aircraft Classification and Acoustic Impact Estimation Based on Real-Time Take-off Noise Measurements

Sánchez-Fernández, Luis Pastor; Pérez, Luis Alejandro Sánchez; Hernández, José Juan Carbajal; Ruiz, Arturo Rojo

doi:10.1007/s11063-012-9258-5

Cited by 18 publications

(16 citation statements)

References 7 publications

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“…From the results by using the decision module, Table 2 shows the result of testing set patterns for our computational model labeled as ''proposed model''. It shows the comparison with the ''existing model'' proposed by Sánchez et al [24] which features extraction including a LPC based technique and octave analysis. An improvement is observed in classification due to patterns added with the feature extraction method which allowed obtaining data related to human auditory perception (MFCC).…”

Section: Resultsmentioning

confidence: 99%

“…The database implemented by Sánchez et al [24] was used for training the NNs and testing the computational model. Both NNs were trained using the Levenberg-Marquardt algorithm [41,42], according to the feature values computed by octave analysis (see Section 2.2.3) with a total of 96 features for the first feed forward network and the MFCC analysis (see Section 2.2.4) for the second feed forward network with a total of 40 features.…”

Section: Resultsmentioning

confidence: 99%

“…However, these are also used to solve other issues, Asensio et al [23] used the MFCC technique for continuously tracking the similarity of the input sound and the aircraft's sounds, Sánchez et al [24] used a LPC technique for acoustic impact estimation. Fig.…”

Section: Signal Features Extractionmentioning

confidence: 99%

“…Recent approaches propose the use of 1/12 octave analysis [24]. Furthermore, in this work more features are extracted in order to improve classification, 1/24 octave analysis provided 2% higher classification accuracy compared when using 1/12 octave analysis octave.…”

Section: Octave Analysismentioning

confidence: 99%

“…Recently, a classification model [24] has been used to identify a significant number of aircraft class take-offs. The features extraction is made for the entire signal representing a take-off event using linear predictive coding (LPC) to extract 140 features.…”

Section: Introductionmentioning

confidence: 99%

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CycleDRUMS: automatic drum arrangement for bass lines using CycleGAN

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The two main research threads in computer-based music generation are the construction of autonomous music-making systems and the design of computer-based environments to assist musicians. In the symbolic domain, the key problem of automatically arranging a piece of music was extensively studied, while relatively fewer systems tackled this challenge in the audio domain. In this contribution, we propose CycleDRUMS, a novel method for generating drums given a bass line. After converting the waveform of the bass into a mel-spectrogram, we can automatically generate original drums that follow the beat, sound credible, and be directly mixed with the input bass. We formulated this task as an unpaired image-to-image translation problem, and we addressed it with CycleGAN, a well-established unsupervised style transfer framework designed initially for treating images. The choice to deploy raw audio and mel-spectrograms enabled us to represent better how humans perceive music and to draw sounds for new arrangements from the vast collection of music recordings accumulated in the last century. In the absence of an objective way of evaluating the output of both generative adversarial networks and generative music systems, we further defined a possible metric for the proposed task, partially based on human (and expert) judgment. Finally, as a comparison, we replicated our results with Pix2Pix, a paired image-to-image translation network, and we showed that our approach outperforms it.

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Aircraft Classification and Acoustic Impact Estimation Based on Real-Time Take-off Noise Measurements

Cited by 18 publications

References 7 publications

Aircraft take-off noises classification based on human auditory’s matched features extraction

Aircraft take-off noises classification based on human auditory’s matched features extraction

Multi-class Classification of Impulse and Non-impulse Sounds Using Deep Convolutional Neural Network (DCNN)

CycleDRUMS: automatic drum arrangement for bass lines using CycleGAN

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