In recent years, Optical Remote Sensing Image Salient Object Detection (ORSI-SOD) has made substantial progress. Nevertheless, it remains an open-ended research area with complex challenges. Most existing ORSI-SOD methods, aiming for high-performance detection, demand large-scale parameters and high computational costs. This significantly restricts their application on resource-constrained devices, which have limited computing power and memory capacity. To tackle this issue, we propose a lightweight and highly efficient ORSI-SOD network, termed RAMENet. With only 5.18M parameters and 8.72G FLOPs, RAMENet can achieve competitive detection accuracy compared to state-of-the-art methods. Specifically, we devise a Dynamic Region-aware Block (DRB) that can be nested within the encoder to realize plug-and-play functionality. This enables the network to learn ORSI domain-specific feature representations, thus more effectively locating salient object regions. Furthermore, we present a novel Multi-path Enhanced M-shaped Decoder (MED), which integrates both bottom-up and top-down paradigms. Comprising two feature extraction sub-branches and a master feature refinement branch, this architecture achieves multi-granularity feature aggregation via cross-level feature interaction. Consequently, it significantly improves the detailed representation capability while maintaining the integrity of the object structure. Extensive experimental results indicate that the RAMENet outperforms 5 state-of-the-art lightweight methods in terms ofSα,Fβmean,MAEon EORSSD and ORSSD datasets, with improvement reaching 0.68%, 0.92%, 0.13%, 0.60%, 1.13%, and 0.07%, respectively. The code and results are available at https://github.com/hjy0518/RAMENet/.

The unique imaging conditions of satellites introduce significant uncertainties in the structure and scale of ground objects, presenting a major challenge for Optical Remote Sensing Image Salient Object Detection (ORSI-SOD). Current ORSI-SOD methods often fail to effectively differentiate between salient objects and subtle background variations, leading to suboptimal prediction outcomes. Furthermore, ORSI-SOD is a dense pixel prediction task, and existing approaches frequently depend on pixel-level probabilities, which can result in overconfident and inaccurate predictions. To address these challenges, we reformulate the ORSI-SOD task as a mask-generation problem by introducing a novel paradigm and propose a diffusion model-based method for ORSI-SOD, termed ORSIDiff. Central to our approach is the design of a powerful denoising network that enhances the model’s refinement capabilities. This network leverages the strengths of both global and local modeling, improving the handling of salient object details and enabling a deeper understanding of the distinctions between salient objects and their surroundings. Additionally, we introduce a consistency assessment strategy that aggregates multiple potential predictions during the denoising process, effectively mitigating the issue of overconfident point estimation. Extensive experimental results on two widely used ORSI-SOD datasets demonstrate that ORSIDiff achieves significant performance improvements over 20 state-of-the-art methods.

The role of Camouflaged Object Detection (COD) is to identify the objects that integrate seamlessly with the surrounding environment. Due to the high intrinsic similarity between the objects and their background, this task presents greater challenges than traditional object detection. Most existing COD methods often have a large number of parameters and high computational complexity in the pursuit of detection accuracy, which hinders the application of COD in practical scenarios. To address this issue, we propose a UNet-like Transformer Network for COD, termed UTNet, which achieves competitive detection accuracy with a smaller parameter set. Specifically, we propose a Camouflaged Region Awareness Module (CRAM) consisting of a Hierarchical Attention Mechanism (HAM) that groups features to reveal intrinsic consistency between sub-features. This CRAM can be embedded into the backbone network, giving it powerful modeling capabilities. And, we present a Contextual Knowledge Collector (CKC) that exploits a cross-aggregation approach for neighboring feature layers, promoting the flow of semantic information from high-level to low-level features, and ensuring the integrity of camouflaged objects at each level of features. Furthermore, we introduce a progressive decoder that utilizes a cascade of attention units to filter noise and explores knowledge aggregation to emphasize features from different levels, ensuring that camouflaged objects have complete spatial details at the local level. Extensive experimental results show that UTNet achieves competitive results compared to 20 state-of-the-art methods. Codes and results are released onhttps://github.com/hjy0518/UTNet.

This paper focuses on the development and analysis of an innovative unmanned soil excavating and transporting system, utilizing three-dimensional point cloud technology to enhance automation and productivity in earthwork operations. The study introduces a 3D point cloud-based volume estimation technique for real-time monitoring and measurement of exca-vation sites, as well as a model for controlling soil unloading volumes in conjunction with vehicle speed to ensure precise and efficient soil handling. Earthmoving in construction sites, particularly soil excavation and placement, is a critical yet risky endeavor due to manual operation inefficiencies. Therefore, the creation of a safer, unmanned soil handling system is vital for augmenting the safety and efficiency of construction operations.

The Philips audio fingerprint[1] has been used for years, but its robustness against external noise has not been studied accurately. This paper shows the Philips fingerprint is noise resistant, and is capable of recognizing music that is corrupted by noise at a -4 to -7 dB signal to noise ratio. In addition, the drawbacks of the Philips fingerprint are addressed by utilizing a “Power Mask” in conjunction with the Philips fingerprint during the matching process. This Power Mask is a weight matrix given to the fingerprint bits, which allows mismatched bits to be penalized according to their relevance in the fingerprint. The effectiveness of the proposed fingerprint was evaluated by experiments using a database of 1030 songs and 1184 query files that were heavily corrupted by two types of noise at varying levels. Our experiments show the proposed method has significantly improved the noise resistance of the standard Philips fingerprint.

Suppose that you are at a music festival checking on an artist, and you would like to quickly know about the song that is being played (e.g., title, lyrics, album, etc.). If you have a smartphone, you could record a sample of the live performance and compare it against a database of existing recordings from the artist. Services such as Shazam or SoundHound will not work here, as this is not the typical framework for audio fingerprinting or query-by-humming systems, as a live performance is neither identical to its studio version (e.g., variations in instrumentation, key, tempo, etc.) nor it is a hummed or sung melody. We propose an audio fingerprinting system that can deal with live version identification by using image processing techniques. Compact fingerprints are derived using a log-frequency spectrogram and an adaptive thresholding method, and template matching is performed using the Hamming similarity and the Hough Transform.

Multi-pitch analysis of concurrent sound sources is an important but challenging problem. It requires estimating pitch values of all harmonic sources in individual frames and streaming the pitch estimates into trajectories, each of which corresponds to a source. We address the streaming problem for monophonic sound sources. We take the original audio, plus frame-level pitch estimates from any multi-pitch estimation algorithm as inputs, and output a pitch trajectory for each source. Our approach does not require pre-training of source models from isolated recordings. Instead, it casts the problem as a constrained clustering problem, where each cluster corresponds to a source. The clustering objective is to minimize the timbre inconsistency within each cluster. We explore different timbre features for music and speech. For music, harmonic structure and a newly proposed feature called uniform discrete cepstrum (UDC) are found effective; while for speech, MFCC and UDC works well. We also show that timbre-consistency is insufficient for effective streaming. Constraints are imposed on pairs of pitch estimates according to their time-frequency relationships. We propose a new constrained clustering algorithm that satisfies as many constraints as possible while optimizing the clustering objective. We compare the proposed approach with other state-of-the-art supervised and unsupervised multi-pitch streaming approaches that are specifically designed for music or speech. Better or comparable results are shown.

We propose a new approach for automatic melody extraction from polyphonic audio, based on Probabilistic Latent Component Analysis (PLCA). An audio signal is first divided into vocal and non-vocal segments using a trained Gaussian Mixture Model (GMM) classifier. A statistical model of the non-vocal segments of the signal is then learned adaptively from this particular input music by PLCA. This model is then employed to remove the accompaniment from the mixture, leaving mainly the vocal components. The melody line is extracted from the vocal components using an auto-correlation algorithm. Quantitative evaluation shows that the new system performs significantly better than two existing melody extraction algorithms for polyphonic single-channel mixtures.

In mixtures of musical sounds, the problem of overlapped harmonics poses a significant challenge to source separation. Common Amplitude Modulation (CAM) is one of the most effective methods to resolve this problem. It, however, relies on non-overlapped harmonics from the same note being available. We propose an alternate technique for harmonic envelope estimation, based on Harmonic Temporal Envelope Similarity (HTES). We learn a harmonic envelope model for each instrument from the non-overlapped harmonics of notes of the same instrument, wherever they occur in the recording. This model is used to reconstruct the harmonic envelopes for overlapped harmonics. This allows reconstruction of completely overlapped notes. Experiments show our algorithm performs better than an existing system based on CAM when the harmonics of pitched instruments are strongly overlapped.

Given a set of monophonic, harmonic sound sources (e.g. human voices or wind instruments), multi-pitch estimation (MPE) is the task of determining the instantaneous pitches of each source. Multi-pitch tracking (MPT) connects the instantaneous pitch estimates provided by MPE algorithms into pitch trajectories of sources. A trajectory can be short (within a musical note), or long (an entire piece of music). While note-level MPT methods usually utilize local time-frequency proximity of pitches to connect them into a note, song-level MPT is much more difficult and needs more information. This is because pitches evolve discontinuously from note to note, and pitch trajectories can even interweave. In this paper, we cast the song-level MPT problem as a constrained clustering problem. The constraints are time-frequency locality of pitches and the clustering objective is their timbre consistency. Due to this problem's unique properties, existing constrained clustering algorithms cannot be directly applied. We propose a new constrained clustering algorithm. Experiments show that our approach produces good results on real-world music recordings of 4 musical instruments.

Grapheme-to-phoneme (G2P) conversion is an important component in TTS systems. The difficulty in Chinese G2P conversion is to disambiguate the polyphones. In this paper, we formulate the polyphone disambiguation problem into a classification problem and propose a language independent classifier based on maximum entropy to address the issue. Furthermore, we introduce inequality smoothing to alleviate data sparseness and exploit language independent character features as linguistic knowledge. Experimental results show that the character features perform as well as the language dependent features such as words and part-of-speech, compared with the widely-used Gaussian smoothing, the inequality smoothing can greatly reduce the active features used in the classifier and achieve better performance. Our classifier achieves 96.35% in term of overall accuracy, greatly superior to 81.22% by using high-frequent "pin-yin"(Romanization of Chinese phoneme). Finally, we explore to merge all key polyphones into 6 groups and find that the overall accuracy only decreases about 2% and the active features are reduced more than 33% further.