How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective

Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data

Applying of the Algorithm of Lagrange Multipliers in the Removal of Blur in Images

This paper presents a non-iterative method that finds application in a broad scientific field such as digital image restoration. The method, based on algorithm of Lagrange multipliers, is use for the removal of blur in an X-ray caused by uniform linear motion. This method assumes that linear motion corresponds to an integral number of pixels. The resolution of the restored image remains at very high level. The main contribution of the method was found on the Improvement in Signal to Noise Ration (ISNR) that has been increase significantly compared to the classic technique

Using of the Algorithm of Lagrange Multipliers in Image Restoration

This paper presents a method based on algorithm of Lagrange multipliers. The method is use for the removal of blur in an X-ray caused by uniform linear motion. This method assumes that linear motion corresponds to an integral number of pixels. The resolution of the restored image remains at very high level. The main contribution of the method was found on the Improvement in Signal to Noise Ration (ISNR) that has been increase significantly compared to the classic techniques

Application of the Progressive Wavelet Correlation to Content-Based Image Retrieving

The following study presents a method for search and retrieval of images from massive image collections. The method consists of two phases. The first phase uses well-known methods of image searching by descriptors based on the content of the searched image. In the second phase the progressive wavelet correlation method is applied on the small number of image candidates selected in previous search phase. The final search result is the wanted image, if it is in the data base. Experiments are performed with data bases of 1000 and 10 000 images

Time Encoded Signal Processing for Speech Quality Assessment

In this paper a method for speech quality assessment is described and evaluated simulating transmission of AMR-NB encoded speech over noisy GSM channel. The proposed system uses comparison of Time Encoded Signal (TES) processing of speech sequences, where one original and one degraded speech signal were transmitted trough GSM simulation system with AWGN noise channel. Several tests have been made on reference speech sample of single speaker with simulated bit-error loss effects on the perceived speech. The achieved results and the similarity measure scores between two TESspeech sequences for various levels of noise channel conditions were compared with measured PESQ MOS values of the used channel and the correlation between them was observed

Speech Synthesis of Dissimilar Languages Using their Phonetic Superset

The two most common languages in Macedonia are Macedonian and Albanian language. The main idea of this paper is to upgrade the phonetic inventory of Macedonian language to its superset – phonetic inventory of the Albanian language, and use it for speech synthesis in both languages. The structure of general speech synthesis system for the Macedonian language is used [1] for its upgrade to speech synthesis system, which will produce Albanian language also. The modification of the system is described and there are details about the specific features of the languages important for the speech synthesis, like sound and vowel system and prosodic elements. Because concatenative speech synthesizers are based on a collection of speech segments, the creation of common speech corpus for these languages is described

Speech Quality Measurement in GSM Networks Using Time Encoded Signal Processing

In this paper a method for speech quality estimation was evaluated simulating transmission of AMR-NB encoded speech over noisy GSM channel. The proposed system uses comparison of Time Encoded Signal (TES) processing of speech sequences, where one original and one degraded speech signal were transmitted trough GSM simulation system with AWGN noise channel. Several tests have been made on reference speech sample of single speaker with simulated bit-error loss effects on the perceived speech. The achieved results and the similarity measure scores between two TES speech sequences for various levels of noise channel conditions were compared with measured PESQ MOS values of the used channel and the correlation between them was observed

Synthesized Speech Quality Evaluation Using ITU-T P.563

In this paper a method for speech quality evaluation of TTS system is presented and its usability is assessed. The ITU-T P.563 is used as a reference-free objective measurement method for speech sequences synthesized by concatenative TTS system. The method was examined and the achieved results were compared to those measured by subjective auditory tests and their correlation values were observed. It was shown that this method is useful for automatic evaluation of synthetic speech quality after major revisions of TTS systems, without the need for preparation and execution of time consuming and expensive subjective tests